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2026
Conference article
Open Access
Generalizing hypergraph Ego-networks and their temporal stability
Cauteruccio Francesco, Citraro Salvatore, Failla Andrea, Rossetti Giulio
Ego-networks provide a local perspective on networked systems by focusing on a central entity and its immediate relational context. While extensively studied in static pairwise interaction models, their analysis in dynamic and higher-order interaction contexts remains limited. In this paper, we introduce a formal analytical framework to generalize ego-networks to temporal hypergraphs, capable of modeling complex, non-dyadic group interactions over time. Our framework consists of two main components, namely, the concept of Rooted Ego-Networks (RENs), and generalized similarity criterion. RENs extend traditional ego-networks by capturing temporal and structural characteristics of groups centered on one or more nodes. We define multiple inclusion functions and similarity criteria to compare RENs across time, and we introduce the notion of stability to identify persistent local structures. Through empirical evaluation on real-world temporal hypergraphs from the SocioPatterns database, we illustrate the expressiveness of our approach in capturing and analyzing the evolution of localized group dynamics. To the best of our knowledge, this is the first comprehensive framework for analyzing the temporal and non-monotonic evolution of ego structures in higher-order networks.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16322, pp. 53-67. Niagara Falls, ON, Canada, 25-28/08/2025
DOI: 10.1007/978-3-032-13513-1_5
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
Cauteruccio Francesco, Citraro Salvatore, Failla Andrea, Rossetti Giulio
Ego-networks provide a local perspective on networked systems by focusing on a central entity and its immediate relational context. While extensively studied in static pairwise interaction models, their analysis in dynamic and higher-order interaction contexts remains limited. In this paper, we introduce a formal analytical framework to generalize ego-networks to temporal hypergraphs, capable of modeling complex, non-dyadic group interactions over time. Our framework consists of two main components, namely, the concept of Rooted Ego-Networks (RENs), and generalized similarity criterion. RENs extend traditional ego-networks by capturing temporal and structural characteristics of groups centered on one or more nodes. We define multiple inclusion functions and similarity criteria to compare RENs across time, and we introduce the notion of stability to identify persistent local structures. Through empirical evaluation on real-world temporal hypergraphs from the SocioPatterns database, we illustrate the expressiveness of our approach in capturing and analyzing the evolution of localized group dynamics. To the best of our knowledge, this is the first comprehensive framework for analyzing the temporal and non-monotonic evolution of ego structures in higher-order networks.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 16322, pp. 53-67. Niagara Falls, ON, Canada, 25-28/08/2025
DOI: 10.1007/978-3-032-13513-1_5
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
See at:
CNR IRIS 
| link.springer.com | doi.org 
| CNR IRIS | CNR IRIS
2026
Journal article
Open Access
Reddit depression communities as spaces of emotion regulation: a data-informed analysis of coping and engagement
Morini Virginia, Citraro Salvatore, Sajno Elena, Sansoni Maria, Riva Giuseppe, Stella Massimo, Rossetti Giulio
Online social platforms increasingly function as informal self-help environments for individuals experiencing depression, offering spaces for emotional expression and peer support outside traditional clinical settings. However, how coping strategies and psychological engagement states—individuals’ emotional and cognitive involvement in managing their condition—are reflected through online self-disclosure remains poorly understood. We analyzed a large-scale dataset from Reddit depression-related communities to investigate how different psycho-linguistic profiles and coping orientations emerge from users’ language. We collected posts and comments from over 300,000 users across six depression-focused subreddits over two years. User-generated text was characterized through multiple psychological and linguistic dimensions capturing emotions, sentiment, subjectivity, and related features, then aggregated at the user-month level and analyzed using unsupervised clustering techniques. Our analysis identifies four distinct groups characterized by different emotional profiles and dominant coping orientations. These states exhibit meaningful correspondences with established theoretical frameworks, including the Coping Orientations to Problems Experienced model and the Patient Health Engagement model. Our findings demonstrate that large-scale textual data from online communities can provide interpretable insights into coping behaviors and engagement patterns, offering a complementary perspective to traditional approaches for studying mental health.Source: FUTURE INTERNET, vol. 18 (issue 4)
DOI: 10.3390/fi18040198
Project(s): Future Artificial Intelligence Research, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
Morini Virginia, Citraro Salvatore, Sajno Elena, Sansoni Maria, Riva Giuseppe, Stella Massimo, Rossetti Giulio
Online social platforms increasingly function as informal self-help environments for individuals experiencing depression, offering spaces for emotional expression and peer support outside traditional clinical settings. However, how coping strategies and psychological engagement states—individuals’ emotional and cognitive involvement in managing their condition—are reflected through online self-disclosure remains poorly understood. We analyzed a large-scale dataset from Reddit depression-related communities to investigate how different psycho-linguistic profiles and coping orientations emerge from users’ language. We collected posts and comments from over 300,000 users across six depression-focused subreddits over two years. User-generated text was characterized through multiple psychological and linguistic dimensions capturing emotions, sentiment, subjectivity, and related features, then aggregated at the user-month level and analyzed using unsupervised clustering techniques. Our analysis identifies four distinct groups characterized by different emotional profiles and dominant coping orientations. These states exhibit meaningful correspondences with established theoretical frameworks, including the Coping Orientations to Problems Experienced model and the Patient Health Engagement model. Our findings demonstrate that large-scale textual data from online communities can provide interpretable insights into coping behaviors and engagement patterns, offering a complementary perspective to traditional approaches for studying mental health.Source: FUTURE INTERNET, vol. 18 (issue 4)
DOI: 10.3390/fi18040198
Project(s): Future Artificial Intelligence Research, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
See at:
CNR IRIS | www.mdpi.com | CNR IRIS
2026
Journal article
Open Access
SpreadPy: A Python tool for modelling spreading activation and superdiffusion in cognitive multiplex networks
Citraro Salvatore, Haim Edith, Carini Alessandra, Siew Cynthia S. Q., Rossetti Giulio, Stella Massimo
We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool enables systematic testing of structure–function relationships in cognition by comparing simulated activation dynamics outcomes with established theories in knowledge modelling. We demonstrate the library’s utility through three case studies: (1) Spreading activation on associative knowledge networks distinguishes students with high versus low math anxiety, revealing anxiety-related structural differences in conceptual organization; (2) Activation trajectories in a creativity task vary with task difficulty, revealing how cognitive load modulates lexical access; (3) In individuals with aphasia, simulated activation patterns on lexical networks correlate with empirical error types (semantic vs. phonological) during picture-naming tasks, linking network structure to clinical impairments. SpreadPy’s flexible framework allows researchers to model these processes using empirically derived or theoretical networks, providing mechanistic insights into individual differences and cognitive impairments. The library is openly available, supporting reproducible research in psychology, neuroscience, and education research.Source: COMPUTERS IN HUMAN BEHAVIOR REPORTS, vol. 21 (issue 100940)
DOI: 10.1016/j.chbr.2026.100940
DOI: 10.48550/arxiv.2507.09628
Project(s): COGNASCO/COGNOSCO, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
Citraro Salvatore, Haim Edith, Carini Alessandra, Siew Cynthia S. Q., Rossetti Giulio, Stella Massimo
We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool enables systematic testing of structure–function relationships in cognition by comparing simulated activation dynamics outcomes with established theories in knowledge modelling. We demonstrate the library’s utility through three case studies: (1) Spreading activation on associative knowledge networks distinguishes students with high versus low math anxiety, revealing anxiety-related structural differences in conceptual organization; (2) Activation trajectories in a creativity task vary with task difficulty, revealing how cognitive load modulates lexical access; (3) In individuals with aphasia, simulated activation patterns on lexical networks correlate with empirical error types (semantic vs. phonological) during picture-naming tasks, linking network structure to clinical impairments. SpreadPy’s flexible framework allows researchers to model these processes using empirically derived or theoretical networks, providing mechanistic insights into individual differences and cognitive impairments. The library is openly available, supporting reproducible research in psychology, neuroscience, and education research.Source: COMPUTERS IN HUMAN BEHAVIOR REPORTS, vol. 21 (issue 100940)
DOI: 10.1016/j.chbr.2026.100940
DOI: 10.48550/arxiv.2507.09628
Project(s): COGNASCO/COGNOSCO, Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
See at:
arXiv.org e-Print Archive | Computers in Human Behavior Reports | CNR IRIS | www.sciencedirect.com | doi.org | CNR IRIS
2026
Journal article
Open Access
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and AI-simulated raters
Haim Edith, Fischer Natalie, Citraro Salvatore, Rossetti Giulio, Stella Massimo
Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human-, GPT3.5-, and Sonnet 3.7-generated stories. Using Explainable Artificial Intelligence (XAI) we test whether features relative to Mednick’s associative theory of creativity can explain creativity ratings assigned by humans or AI raters. Using XGBoost, we examine 5 scenarios: (i) human rating human stories, (ii) GPT-3.5 rating human stories, (iii) GPT-3.5 rating GPT-3.5 stories, (iv) Sonnet 3.7 rating human stories, and (v) Sonnet 3.7 rating Sonnet 3.7 stories. Our findings reveal that GPT-3.5 and Sonnet 3.7 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 and Sonnet 3.7 favour “their own” stories and rate human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for ratings by GPT-3.5 and Sonnet 3.7 for human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 and Sonnet 3.7 rating their own stories. These quantitative results underscore key limitations in the ability of GPT-3.5 and Sonnet 3.7 to align with human assessments of creativity. We emphasise the need for caution when using AI models to assess and generate creative content, as they may not yet capture the nuanced complexity that characterises human creativity.Source: JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, vol. 9 (issue 1)
DOI: 10.1007/s42001-025-00446-z
DOI: 10.31234/osf.io/6zpre_v2
DOI: 10.31234/osf.io/6zpre
DOI: 10.31234/osf.io/6zpre_v1
DOI: 10.31234/osf.io/6zpre_v3
DOI: 10.48550/arxiv.2412.00530
Metrics:
Haim Edith, Fischer Natalie, Citraro Salvatore, Rossetti Giulio, Stella Massimo
Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human-, GPT3.5-, and Sonnet 3.7-generated stories. Using Explainable Artificial Intelligence (XAI) we test whether features relative to Mednick’s associative theory of creativity can explain creativity ratings assigned by humans or AI raters. Using XGBoost, we examine 5 scenarios: (i) human rating human stories, (ii) GPT-3.5 rating human stories, (iii) GPT-3.5 rating GPT-3.5 stories, (iv) Sonnet 3.7 rating human stories, and (v) Sonnet 3.7 rating Sonnet 3.7 stories. Our findings reveal that GPT-3.5 and Sonnet 3.7 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 and Sonnet 3.7 favour “their own” stories and rate human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for ratings by GPT-3.5 and Sonnet 3.7 for human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 and Sonnet 3.7 rating their own stories. These quantitative results underscore key limitations in the ability of GPT-3.5 and Sonnet 3.7 to align with human assessments of creativity. We emphasise the need for caution when using AI models to assess and generate creative content, as they may not yet capture the nuanced complexity that characterises human creativity.Source: JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE, vol. 9 (issue 1)
DOI: 10.1007/s42001-025-00446-z
DOI: 10.31234/osf.io/6zpre_v2
DOI: 10.31234/osf.io/6zpre
DOI: 10.31234/osf.io/6zpre_v1
DOI: 10.31234/osf.io/6zpre_v3
DOI: 10.48550/arxiv.2412.00530
Metrics:
See at:
arXiv.org e-Print Archive | Journal of Computational Social Science | doi.org | doi.org | doi.org | doi.org | IRIS - Institutional Research Information System of the University of Trento | CNR IRIS | IRIS - Institutional Research Information System of the University of Trento | doi.org | CNR IRIS
2025
Conference article
Restricted
Structure-attribute similarity interplay in diffusion dynamics on social networks
Citraro S., Pansanella V., Rossetti G.
Social interactions are shaped by homophily, the tendency for individuals to connect with others who share similar attributes. Exploring this phenomenon is crucial for understanding a wide spectrum of social behaviors, including the spread of misinformation and the dynamics of societal debates. In this study, we leverage a graph transformation strategy—which analyzes the interplay between individuals’ personal preferences and their structural connections—to investigate mechanisms of opinion/information diffusion. Among these latter ones, we focus on the Deffuant-Weisbuch model to simulate opinion dynamics and the Independent Cascade model to simulate information spread. Our findings on real-world social networks suggest that emphasizing attribute similarities enhances graph cohesion, whereas forcing structural similarities leads to fragmentation. Moreover, we observe a trend towards consensus opinion formation when enhancing attribute similarities, and faster as well as complete coverage of information spread in the same setup. These results motivate the importance of considering both individual attributes and network structure in studying social dynamics.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15244, pp. 425-439. ita, 2024
DOI: 10.1007/978-3-031-78980-9_27
Metrics:
Citraro S., Pansanella V., Rossetti G.
Social interactions are shaped by homophily, the tendency for individuals to connect with others who share similar attributes. Exploring this phenomenon is crucial for understanding a wide spectrum of social behaviors, including the spread of misinformation and the dynamics of societal debates. In this study, we leverage a graph transformation strategy—which analyzes the interplay between individuals’ personal preferences and their structural connections—to investigate mechanisms of opinion/information diffusion. Among these latter ones, we focus on the Deffuant-Weisbuch model to simulate opinion dynamics and the Independent Cascade model to simulate information spread. Our findings on real-world social networks suggest that emphasizing attribute similarities enhances graph cohesion, whereas forcing structural similarities leads to fragmentation. Moreover, we observe a trend towards consensus opinion formation when enhancing attribute similarities, and faster as well as complete coverage of information spread in the same setup. These results motivate the importance of considering both individual attributes and network structure in studying social dynamics.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15244, pp. 425-439. ita, 2024
DOI: 10.1007/978-3-031-78980-9_27
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
2025
Journal article
Open Access
Characterizing user archetypes and discussions on social hypernetworks
Failla A., Citraro S., Rossetti G., Cauteruccio F.
In recent years, the proliferation of social platforms has drastically transformed how individuals interact, organize, and share information. In this scenario, there has been an unprecedented increase in the scale and complexity of interactions and, at the same time, little to no research about certain fringe social platforms. In this paper, we present a multi-dimensional framework for characterizing nodes and hyperedges in social hypernetworks, with a focus on the understudied alt-right platform Scored.co. Our approach integrates the possibility of studying higher-order interactions, thanks to the hypernetwork representation, and various node features such as user activity, sentiment, and toxicity, with the aim of defining distinct user archetypes and understanding their roles within the network. Utilizing a comprehensive dataset from Scored.co, consisting of more than 4.4 M posts and 36.9 M comments, we analyze the dynamics of these archetypes over time and explore their interactions and influence within the community. We identify eight archetypes, with the largest group comprising over 15,000 users, and observe that 44% of interactions involve at least five participants, highlighting the importance of higher-order modeling. Furthermore, we find significant archetype transitions and stable yet locally dense interaction patterns, with users exposed to roughly 1000 unique peers on average. The framework’s versatility allows for detailed analysis of both individual user behaviors and broader social structures. Our findings highlight the importance of higher-order interactions and node features in understanding social dynamics, and offer new insights into the roles and behaviors that emerge in complex online environments.Source: BIG DATA AND COGNITIVE COMPUTING, vol. 9 (issue 9)
DOI: 10.3390/bdcc9090236
Metrics:
Failla A., Citraro S., Rossetti G., Cauteruccio F.
In recent years, the proliferation of social platforms has drastically transformed how individuals interact, organize, and share information. In this scenario, there has been an unprecedented increase in the scale and complexity of interactions and, at the same time, little to no research about certain fringe social platforms. In this paper, we present a multi-dimensional framework for characterizing nodes and hyperedges in social hypernetworks, with a focus on the understudied alt-right platform Scored.co. Our approach integrates the possibility of studying higher-order interactions, thanks to the hypernetwork representation, and various node features such as user activity, sentiment, and toxicity, with the aim of defining distinct user archetypes and understanding their roles within the network. Utilizing a comprehensive dataset from Scored.co, consisting of more than 4.4 M posts and 36.9 M comments, we analyze the dynamics of these archetypes over time and explore their interactions and influence within the community. We identify eight archetypes, with the largest group comprising over 15,000 users, and observe that 44% of interactions involve at least five participants, highlighting the importance of higher-order modeling. Furthermore, we find significant archetype transitions and stable yet locally dense interaction patterns, with users exposed to roughly 1000 unique peers on average. The framework’s versatility allows for detailed analysis of both individual user behaviors and broader social structures. Our findings highlight the importance of higher-order interactions and node features in understanding social dynamics, and offer new insights into the roles and behaviors that emerge in complex online environments.Source: BIG DATA AND COGNITIVE COMPUTING, vol. 9 (issue 9)
DOI: 10.3390/bdcc9090236
Metrics:
See at:
Big Data and Cognitive Computing | CNR IRIS | www.mdpi.com | Archivio della Ricerca - Università di Salerno | Archivio della Ricerca - Università di Salerno | CNR IRIS | Archivio della Ricerca - Università di Salerno
2025
Conference article
Open Access
Burstiness in emotions: a case study on collective affective responses in Italian soccer fandoms
Citraro S., Mauro G., Ferragina E.
The bursty nature of emotions is rarely investigated outside cognitive and psychological studies. Therefore this work addresses a gap in the literature, investigating the phenomenon of emotional burstiness using tools from the analysis of complex systems, and considering as case-study soccer fans’ affective responses on social media. We reconstruct collective reactions on Instagram posts from official accounts of 40 Italian football teams during the first round of the 2023–2024 season – 20 teams from Serie B (the second tier of Italian Football) and the 20 most followed teams in Serie C (the third tier). With this data, we build sequences of emotional signals for four types of emotions: joy, anger, sadness, and fear. Our analysis reveals trends of anti-burstiness in expressions of joy among users, reflecting fans’ consistent support for teams, occasionally interspersed by bursts of anger and sadness, with no signals of fear. This preliminary investigation provides insights for the understanding of emotional dynamics in online discussions and team supporting in soccer leagues.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15211, pp. 56-69. Rende, Cosenza, Italy, 2–5/09/2024
DOI: 10.1007/978-3-031-78541-2_4
Metrics:
Citraro S., Mauro G., Ferragina E.
The bursty nature of emotions is rarely investigated outside cognitive and psychological studies. Therefore this work addresses a gap in the literature, investigating the phenomenon of emotional burstiness using tools from the analysis of complex systems, and considering as case-study soccer fans’ affective responses on social media. We reconstruct collective reactions on Instagram posts from official accounts of 40 Italian football teams during the first round of the 2023–2024 season – 20 teams from Serie B (the second tier of Italian Football) and the 20 most followed teams in Serie C (the third tier). With this data, we build sequences of emotional signals for four types of emotions: joy, anger, sadness, and fear. Our analysis reveals trends of anti-burstiness in expressions of joy among users, reflecting fans’ consistent support for teams, occasionally interspersed by bursts of anger and sadness, with no signals of fear. This preliminary investigation provides insights for the understanding of emotional dynamics in online discussions and team supporting in soccer leagues.Source: LECTURE NOTES IN COMPUTER SCIENCE, vol. 15211, pp. 56-69. Rende, Cosenza, Italy, 2–5/09/2024
DOI: 10.1007/978-3-031-78541-2_4
Metrics:
See at:
Hyper Article en Ligne - Sciences de l'Homme et de la Société | CNR IRIS | link.springer.com | Hyper Article en Ligne - Sciences de l'Homme et de la Société | Hyper Article en Ligne - Sciences de l'Homme et de la Société | Archivio della Ricerca - Università di Pisa | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2024
Journal article
Open Access
Describing group evolution in temporal data using multi-faceted events
Failla A, Cazabet R., Rossetti G., Citraro S.
Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.Source: MACHINE LEARNING, vol. 113 (issue 10), pp. 7591-7615
DOI: 10.1007/s10994-024-06600-4
Project(s): BITUNAM
Metrics:
Failla A, Cazabet R., Rossetti G., Citraro S.
Groups—such as clusters of points or communities of nodes—are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of “events”. However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as “archetypes” characterized by a unique combination of quantitative dimensions that we call “facets”. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.Source: MACHINE LEARNING, vol. 113 (issue 10), pp. 7591-7615
DOI: 10.1007/s10994-024-06600-4
Project(s): BITUNAM
Metrics:
See at:
Machine Learning | CNR IRIS | link.springer.com | CNR IRIS
2024
Journal article
Open Access
Online posting effects: unveiling the non-linear journeys of users in depression communities on Reddit
Morini V., Citraro S., Sajno E., Sansoni M., Riva G., Stella M., Rossetti G.
Social media platforms have become pivotal as self-help forums, enabling individuals to share personal experiences and seek support. However, on topics as sensitive as depression, what are the consequences of online self-disclosure? Here, we delve into the dynamics of mental health discourse on various Reddit boards focused on depression. To this aim, we introduce a data-informed framework reconstructing online dynamics from 303k users interacting over two years. Through user-generated content, we identify 4 distinct clusters representing different psychological states. Our analysis unveils online posting effects: a user can transition to another psychological state after online exposure to peers’ emotional/semantic content. As described by conditional Markov chains and different levels of social exposure, users’ transitions reveal navigation through both positive and negative phases in a spiral rather than a linear progression. Interpreted in light of psychological literature, related particularly to the Patient Health Engagement (PHE) model, our findings can provide evidence that the type and layout of online social interactions have an impact on users’ “journeys” when posting about depression.Source: COMPUTERS IN HUMAN BEHAVIOR REPORTS, vol. 17 (issue 100542 (n. articolo))
DOI: 10.1016/j.chbr.2024.100542
Project(s): SoBigData-PlusPlus
Metrics:
Morini V., Citraro S., Sajno E., Sansoni M., Riva G., Stella M., Rossetti G.
Social media platforms have become pivotal as self-help forums, enabling individuals to share personal experiences and seek support. However, on topics as sensitive as depression, what are the consequences of online self-disclosure? Here, we delve into the dynamics of mental health discourse on various Reddit boards focused on depression. To this aim, we introduce a data-informed framework reconstructing online dynamics from 303k users interacting over two years. Through user-generated content, we identify 4 distinct clusters representing different psychological states. Our analysis unveils online posting effects: a user can transition to another psychological state after online exposure to peers’ emotional/semantic content. As described by conditional Markov chains and different levels of social exposure, users’ transitions reveal navigation through both positive and negative phases in a spiral rather than a linear progression. Interpreted in light of psychological literature, related particularly to the Patient Health Engagement (PHE) model, our findings can provide evidence that the type and layout of online social interactions have an impact on users’ “journeys” when posting about depression.Source: COMPUTERS IN HUMAN BEHAVIOR REPORTS, vol. 17 (issue 100542 (n. articolo))
DOI: 10.1016/j.chbr.2024.100542
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Computers in Human Behavior Reports | CNR IRIS | www.sciencedirect.com | CNR IRIS
2024
Journal article
Open Access
Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges
Stella M., Citraro S., Rossetti G., Marinazzo D., Kenett Y. N., Vitevitch M. S.
The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.Source: PSYCHONOMIC BULLETIN & REVIEW, vol. 31, pp. 1981-2004
DOI: 10.3758/s13423-024-02473-9
Metrics:
Stella M., Citraro S., Rossetti G., Marinazzo D., Kenett Y. N., Vitevitch M. S.
The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.Source: PSYCHONOMIC BULLETIN & REVIEW, vol. 31, pp. 1981-2004
DOI: 10.3758/s13423-024-02473-9
Metrics:
See at:
Psychonomic Bulletin & Review | CNR IRIS | link.springer.com | Ghent University Academic Bibliography | Ghent University Academic Bibliography | IRIS - Institutional Research Information System of the University of Trento | IRIS - Institutional Research Information System of the University of Trento | Ghent University Academic Bibliography | CNR IRIS
2024
Other
Open Access
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters
Haim E., Fischer N., Citraro S., Rossetti G., Stella M.
Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of creativity can explain creativity ratings assigned by humans and GPT-3.5. Using XGBoost, we examine three scenarios: (i) human ratings of human stories, (ii) GPT-3.5 ratings of human stories, and (iii) GPT-3.5 ratings of GPT-generated stories. Our findings reveal that GPT-3.5 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 favours 'its own' stories and rates human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for GPT-3.5's ratings of human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 rating its own stories. These quantitative results underscore key limitations in GPT-3.5's ability to align with human assessments of creativity. We emphasise the need for caution when using GPT-3.5 to assess and generate creative content, as it does not yet capture the nuanced complexity that characterises human creativity.DOI: 10.31234/osf.io/6zpre
DOI: 10.31234/osf.io/6zpre_v2
DOI: 10.31234/osf.io/6zpre_v1
DOI: 10.31234/osf.io/6zpre_v3
DOI: 10.48550/arxiv.2412.00530
Metrics:
Haim E., Fischer N., Citraro S., Rossetti G., Stella M.
Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of creativity can explain creativity ratings assigned by humans and GPT-3.5. Using XGBoost, we examine three scenarios: (i) human ratings of human stories, (ii) GPT-3.5 ratings of human stories, and (iii) GPT-3.5 ratings of GPT-generated stories. Our findings reveal that GPT-3.5 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 favours 'its own' stories and rates human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for GPT-3.5's ratings of human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 rating its own stories. These quantitative results underscore key limitations in GPT-3.5's ability to align with human assessments of creativity. We emphasise the need for caution when using GPT-3.5 to assess and generate creative content, as it does not yet capture the nuanced complexity that characterises human creativity.DOI: 10.31234/osf.io/6zpre
DOI: 10.31234/osf.io/6zpre_v2
DOI: 10.31234/osf.io/6zpre_v1
DOI: 10.31234/osf.io/6zpre_v3
DOI: 10.48550/arxiv.2412.00530
Metrics:
See at:
arXiv.org e-Print Archive | doi.org | doi.org | doi.org | doi.org | CNR IRIS | osf.io | doi.org | CNR IRIS
2023
Journal article
Open Access
Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities
Joseph Sm, Citraro S, Morini V, Rossetti G, Stella M
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding "feel", stem for "to feel" and "feelings", enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of "regret", "unhealthy lifestyle" and "low mental well-being". Importantly, negative associations and emotions co-existed with trustful/positive language, focused on "getting better". This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.Source: PHYSICA. A, vol. 610 (issue 128336)
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
Joseph Sm, Citraro S, Morini V, Rossetti G, Stella M
Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding "feel", stem for "to feel" and "feelings", enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modeling both highlight key narrative topics of "regret", "unhealthy lifestyle" and "low mental well-being". Importantly, negative associations and emotions co-existed with trustful/positive language, focused on "getting better". This emotional polarization provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our network-based comparisons provide quantitative evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valves. Our findings provide an interpretable network-based aid for supporting psychological inquiries of human feelings in digital and clinical settings.Source: PHYSICA. A, vol. 610 (issue 128336)
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | www.sciencedirect.com | Physica A Statistical Mechanics and its Applications | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Feature-rich multiplex lexical networks reveal mental strategies of early language learning
Citraro S, Vitevitch Ms, Stella M, Rossetti G
Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms--fragmented across linguistics, psychology and computer science--by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.Source: SCIENTIFIC REPORTS, vol. 13 (issue 1)
DOI: 10.1038/s41598-022-27029-6
DOI: 10.48550/arxiv.2201.05061
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S, Vitevitch Ms, Stella M, Rossetti G
Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms--fragmented across linguistics, psychology and computer science--by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.Source: SCIENTIFIC REPORTS, vol. 13 (issue 1)
DOI: 10.1038/s41598-022-27029-6
DOI: 10.48550/arxiv.2201.05061
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | Scientific Reports | CNR IRIS | ISTI Repository | www.nature.com | doi.org | CNR IRIS
2023
Other
Restricted
Feature-rich networks: when topology meets semantics
Citraro S
A network can be enriched with attributes that embed extra information into the nodes. A network can even be enriched with information that encodes different layers of links or that tracks a topological evolution as time goes by. A recent unifying term, that of feature-rich networks, aims to keep all these aspects together within a common denomination and towards a common framework of analysis. The scope of this thesis is three-fold: i) acknowledge all those models that integrate non-structural information into a complex network topology; ii) define new methods (algorithms and measures) for feature-rich network mining; iii) test such methods on applied case studies among different domains. We overview the most influential featurerich representations for complex networks: Node-attributed, Multi-layer, and Dynamic models. All of them open many challenges for the improvement of classic complex network tasks, like community detection, synthetic network generation, and measures for capturing networked patterns and behaviors. We question these tasks, and we develop new methods for feature-rich networks. In particular, we propose EVA, a node-attributed community detection algorithm; X-Mark, a node attributed network generator with planted communities; Conformity, for estimating multi-scale mixing patterns; and ?-Conformity, an extension of the previous one on dynamic environments. Then, we test the proposed methods on different domain specific applications. In particular, we focus on feature-rich models of cognition and higher-order dynamic social data with semantic annotations on users. Throughout the work, our main focus is to demonstrate that mining augmented network topologies can provide novel insights in many domains, and that methods for feature-rich networks can unearth patterns that are invisible to structural-only and semantic only data mining.Project(s): SoBigData-PlusPlus
Citraro S
A network can be enriched with attributes that embed extra information into the nodes. A network can even be enriched with information that encodes different layers of links or that tracks a topological evolution as time goes by. A recent unifying term, that of feature-rich networks, aims to keep all these aspects together within a common denomination and towards a common framework of analysis. The scope of this thesis is three-fold: i) acknowledge all those models that integrate non-structural information into a complex network topology; ii) define new methods (algorithms and measures) for feature-rich network mining; iii) test such methods on applied case studies among different domains. We overview the most influential featurerich representations for complex networks: Node-attributed, Multi-layer, and Dynamic models. All of them open many challenges for the improvement of classic complex network tasks, like community detection, synthetic network generation, and measures for capturing networked patterns and behaviors. We question these tasks, and we develop new methods for feature-rich networks. In particular, we propose EVA, a node-attributed community detection algorithm; X-Mark, a node attributed network generator with planted communities; Conformity, for estimating multi-scale mixing patterns; and ?-Conformity, an extension of the previous one on dynamic environments. Then, we test the proposed methods on different domain specific applications. In particular, we focus on feature-rich models of cognition and higher-order dynamic social data with semantic annotations on users. Throughout the work, our main focus is to demonstrate that mining augmented network topologies can provide novel insights in many domains, and that methods for feature-rich networks can unearth patterns that are invisible to structural-only and semantic only data mining.Project(s): SoBigData-PlusPlus
2023
Conference article
Open Access
Attributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit
Failla A, Citraro S, Rossetti G
Complex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups. Yet even such higher-order representations ignore the importance of the rich attributes carried by the nodes. In this work we introduce ASH, an Attributed Stream-Hypernetwork framework to model higher-order temporal networks with attributes on nodes. We leverage ASH to study pairwise and group political discussions on the well-known Reddit platform. Our analysis unveils different patterns while looking at either a pairwise or a higher-order structure for the same phenomena. In particular, we find out that Reddit users tend to surround themselves by like-minded peers with respect to their political leaning when online discussions are proxied by pairwise interactions; conversely, such a tendency significantly decreases when considering nodes embedded in higher-order contexts - that often describe heterophilic discussions.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 150-161. Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_13
Project(s): SoBigData-PlusPlus
Metrics:
Failla A, Citraro S, Rossetti G
Complex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups. Yet even such higher-order representations ignore the importance of the rich attributes carried by the nodes. In this work we introduce ASH, an Attributed Stream-Hypernetwork framework to model higher-order temporal networks with attributes on nodes. We leverage ASH to study pairwise and group political discussions on the well-known Reddit platform. Our analysis unveils different patterns while looking at either a pairwise or a higher-order structure for the same phenomena. In particular, we find out that Reddit users tend to surround themselves by like-minded peers with respect to their political leaning when online discussions are proxied by pairwise interactions; conversely, such a tendency significantly decreases when considering nodes embedded in higher-order contexts - that often describe heterophilic discussions.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 150-161. Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_13
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | doi.org | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Attributed stream hypergraphs: temporal modeling of node-attributed high-order interactions
Failla A, Citraro S, Rossetti G
Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, aims to focus on connectivity between sets of nodes rather than pairs, whereas the second one, that of feature-rich augmentation, incorporates into a network all those elements that are driven by information which is external to the structure, like node properties or the flow of time. This paper proposes a novel toolbox, that of Attributed Stream Hypergraphs (ASHs), unifying both high-order and feature-rich elements for representing, mining, and analyzing complex networks. Applied to social network analysis, ASHs can characterize complex social phenomena along topological, dynamic and attributive elements. Experiments on real-world face-to-face and online social media interactions highlight that ASHs can easily allow for the analyses, among others, of high-order groups' homophily, nodes' homophily with respect to the hyperedges in which nodes participate, and time-respecting paths between hyperedges.Source: APPLIED NETWORK SCIENCE, vol. 8 (issue 1)
DOI: 10.1007/s41109-023-00555-6
DOI: 10.48550/arxiv.2303.18226
Project(s): SoBigData-PlusPlus
Metrics:
Failla A, Citraro S, Rossetti G
Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, aims to focus on connectivity between sets of nodes rather than pairs, whereas the second one, that of feature-rich augmentation, incorporates into a network all those elements that are driven by information which is external to the structure, like node properties or the flow of time. This paper proposes a novel toolbox, that of Attributed Stream Hypergraphs (ASHs), unifying both high-order and feature-rich elements for representing, mining, and analyzing complex networks. Applied to social network analysis, ASHs can characterize complex social phenomena along topological, dynamic and attributive elements. Experiments on real-world face-to-face and online social media interactions highlight that ASHs can easily allow for the analyses, among others, of high-order groups' homophily, nodes' homophily with respect to the hyperedges in which nodes participate, and time-respecting paths between hyperedges.Source: APPLIED NETWORK SCIENCE, vol. 8 (issue 1)
DOI: 10.1007/s41109-023-00555-6
DOI: 10.48550/arxiv.2303.18226
Project(s): SoBigData-PlusPlus
Metrics:
See at:
appliednetsci.springeropen.com | Applied Network Science | CNR IRIS | ISTI Repository | doi.org | CNR IRIS
2023
Journal article
Open Access
Hypergraph models of the mental lexicon capture greater information than pairwise networks for predicting language learning
Citraro S, Warnerwillich J, Battiston F, Siew Csq, Rossetti G, Stella M
Human memory is a complex system that works in associative ways: Reading a cue word can lead to the recollection of associated concepts. The network structure of memory recall patterns has been shown to contain insights about a wide variety of cognitive phenomena, including language acquisition. However, most current network approaches use pairwise connections, i.e. links between only two words at a time. This ignores the possibility that more than two concept representations might be simultaneously associated in memory. We overcome this modelling limitation by introducing cognitive hypergraphs as models of human memory. We model memory recall patterns through word associations from the Small World of Words project for N=6003 concepts (Study 1) and for N=497 concepts (Study 2). In each study we represent word associations as either a pairwise network or a hypergraph. By combining psycholinguistic norms and network centrality measures with machine learning, we quantitatively investigate whether there is any benefit to using the hypergraph model over a pairwise network in predicting test-based age of acquisition norms in children up to age 9 years (Study 1) or normative learning in toddlers up to age 30 months (Study 2, based on CHILDES data). We show that cognitive hypergraphs capture more information than pairwise networks from the same data: Cognitive hypergraphs are considerably more powerful than pairwise networks at predicting age of acquisition trends in toddlers, children and teenagers. Our studies showcase how novel approaches merging artificial intelligence and higher-order interactions can help us understand cognitive development.Source: NEW IDEAS IN PSYCHOLOGY, vol. 71
DOI: 10.1016/j.newideapsych.2023.101034
Metrics:
Citraro S, Warnerwillich J, Battiston F, Siew Csq, Rossetti G, Stella M
Human memory is a complex system that works in associative ways: Reading a cue word can lead to the recollection of associated concepts. The network structure of memory recall patterns has been shown to contain insights about a wide variety of cognitive phenomena, including language acquisition. However, most current network approaches use pairwise connections, i.e. links between only two words at a time. This ignores the possibility that more than two concept representations might be simultaneously associated in memory. We overcome this modelling limitation by introducing cognitive hypergraphs as models of human memory. We model memory recall patterns through word associations from the Small World of Words project for N=6003 concepts (Study 1) and for N=497 concepts (Study 2). In each study we represent word associations as either a pairwise network or a hypergraph. By combining psycholinguistic norms and network centrality measures with machine learning, we quantitatively investigate whether there is any benefit to using the hypergraph model over a pairwise network in predicting test-based age of acquisition norms in children up to age 9 years (Study 1) or normative learning in toddlers up to age 30 months (Study 2, based on CHILDES data). We show that cognitive hypergraphs capture more information than pairwise networks from the same data: Cognitive hypergraphs are considerably more powerful than pairwise networks at predicting age of acquisition trends in toddlers, children and teenagers. Our studies showcase how novel approaches merging artificial intelligence and higher-order interactions can help us understand cognitive development.Source: NEW IDEAS IN PSYCHOLOGY, vol. 71
DOI: 10.1016/j.newideapsych.2023.101034
Metrics:
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CNR IRIS | ISTI Repository | www.sciencedirect.com | New Ideas in Psychology | CNR IRIS | CNR IRIS
2023
Journal article
Open Access
Towards hypergraph cognitive networks as feature-rich models of knowledge
Citraro S, De Deyne S, Stella M, Rossetti G
Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns (e.g. reading "airplane" makes one think of "air " and "pollution", and this is represented by links "airplane"-"air" and "airplane"-"pollution"). Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N = 3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.Source: EPJ DATA SCIENCE, vol. 12 (issue 1)
DOI: 10.1140/epjds/s13688-023-00409-2
Project(s): SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
Citraro S, De Deyne S, Stella M, Rossetti G
Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns (e.g. reading "airplane" makes one think of "air " and "pollution", and this is represented by links "airplane"-"air" and "airplane"-"pollution"). Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N = 3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.Source: EPJ DATA SCIENCE, vol. 12 (issue 1)
DOI: 10.1140/epjds/s13688-023-00409-2
Project(s): SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics
Metrics:
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epjdatascience.springeropen.com | CNR IRIS | ISTI Repository | CNR IRIS
2023
Journal article
Open Access
Cognitive network science reveals bias in GPT-3, GPT-3.5 turbo, and GPT-4 mirroring math anxiety in high-school students
Abramski K, Citraro S, Lombardi L, Rossetti G, Stella M
Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI's models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.Source: BIG DATA AND COGNITIVE COMPUTING, vol. 7 (issue 3)
DOI: 10.3390/bdcc7030124
Metrics:
Abramski K, Citraro S, Lombardi L, Rossetti G, Stella M
Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. In this study, we introduce a novel application of network science and cognitive psychology to understand biases towards math and STEM fields in LLMs from ChatGPT, such as GPT-3, GPT-3.5, and GPT-4. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have negative perceptions of math and STEM fields, associating math with negative concepts in 6 cases out of 10. We observe significant differences across OpenAI's models: newer versions (i.e., GPT-4) produce 5× semantically richer, more emotionally polarized perceptions with fewer negative associations compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.Source: BIG DATA AND COGNITIVE COMPUTING, vol. 7 (issue 3)
DOI: 10.3390/bdcc7030124
Metrics:
See at:
Big Data and Cognitive Computing | CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS