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2023
Journal article
Open Access
Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities
Joseph S. M., 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 (Print) 610 (2023). doi:10.1016/j.physa.2022.128336
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
Joseph S. M., 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 (Print) 610 (2023). doi:10.1016/j.physa.2022.128336
DOI: 10.1016/j.physa.2022.128336
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository 
| Physica A Statistical Mechanics and its Applications 
| www.sciencedirect.com | CNR ExploRA
2023
Journal article
Open Access
Feature-rich multiplex lexical networks reveal mental strategies of early language learning
Citraro S., Vitevitch M. S., 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 (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-022-27029-6
DOI: 10.1038/s41598-022-27029-6
DOI: 10.48550/arxiv.2201.05061
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Vitevitch M. S., 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 (Nature Publishing Group) 13 (2023). doi:10.1038/s41598-022-27029-6
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 | ISTI Repository | www.nature.com | doi.org | CNR ExploRA
2023
Doctoral thesis
Unknown
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
See at: CNR ExploRA
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 8 (2023). doi:10.1007/s41109-023-00555-6
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 8 (2023). doi:10.1007/s41109-023-00555-6
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 | ISTI Repository | doi.org | CNR ExploRA
2023
Journal article
Open Access
Hypergraph models of the mental lexicon capture greater information than pairwise networks for predicting language learning
Citraro S., Warner-Willich J., Battiston F., Siew C. S. Q., 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 71 (2023). doi:10.1016/j.newideapsych.2023.101034
DOI: 10.1016/j.newideapsych.2023.101034
Metrics:
Citraro S., Warner-Willich J., Battiston F., Siew C. S. Q., 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 71 (2023). doi:10.1016/j.newideapsych.2023.101034
DOI: 10.1016/j.newideapsych.2023.101034
Metrics:
See at:
ISTI Repository | New Ideas in Psychology | www.sciencedirect.com | CNR ExploRA
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 12 (2023). doi:10.1140/epjds/s13688-023-00409-2
DOI: 10.1140/epjds/s13688-023-00409-2
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 12 (2023). doi:10.1140/epjds/s13688-023-00409-2
DOI: 10.1140/epjds/s13688-023-00409-2
Metrics:
See at:
epjdatascience.springeropen.com | ISTI Repository | CNR ExploRA
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 7 (2023). doi:10.3390/bdcc7030124
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 7 (2023). doi:10.3390/bdcc7030124
DOI: 10.3390/bdcc7030124
Metrics:
See at:
Big Data and Cognitive Computing | ISTI Repository | www.mdpi.com | CNR ExploRA
2022
Journal article
Open Access
Delta-Conformity: multi-scale node assortativity in feature-rich stream graphs
Citraro S., Milli L., Cazabet R., Rossetti G.
Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the timevarying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: Delta-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes' connectivity along a period of time Delta starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce Delta-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.Source: International Journal of Data Science and Analytics (Print) (2022). doi:10.1007/s41060-077-00175-4
DOI: 10.1007/s41060-077-00175-4
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Milli L., Cazabet R., Rossetti G.
Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the timevarying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topology to propose a multi-scale, path- and time-aware node homophily estimator specifically tied for feature-rich stream graphs: Delta-Conformity. Our measure can capture the homogeneous/heterogeneous tendency of nodes' connectivity along a period of time Delta starting from a given moment in time. Results on face-to-face interaction networks suggest it is possible to track changes in social mixing behaviors that coincide with contextually reasonable everyday patterns, e.g., medical staff disassortative behavior when exposed to patients. In a different domain, that of the Bitcoin Transaction Network, we capture relationships between the quantity of money sent from (and to) different categories/continents and their respective mixing trends over time. All these insights help us to introduce Delta-Conformity as a suitable solution for understanding temporal homophily by capturing the mixing tendency of entities embedded in fine-grained evolving contexts.Source: International Journal of Data Science and Analytics (Print) (2022). doi:10.1007/s41060-077-00175-4
DOI: 10.1007/s41060-077-00175-4
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
2021
Journal article
Open Access
Conformity: a Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
Unveiling the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works have underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Different from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291
DOI: 10.1109/mis.2021.3051291
DOI: 10.48550/arxiv.2012.05195
Project(s): SoBigData-PlusPlus
Metrics:
Rossetti G., Citraro S., Milli L.
Unveiling the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works have underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Different from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.Source: IEEE intelligent systems 36 (2021): 25–34. doi:10.1109/MIS.2021.3051291
DOI: 10.1109/mis.2021.3051291
DOI: 10.48550/arxiv.2012.05195
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | IEEE Intelligent Systems | ieeexplore.ieee.org | IEEE Intelligent Systems | ISTI Repository | doi.org | CNR ExploRA
2021
Journal article
Open Access
X-Mark: a benchmark for node-attributed community discovery algorithms
Citraro S., Rossetti G.
Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.Source: Social Network Analysis and Mining 11 (2021). doi:10.1007/s13278-021-00823-2
DOI: 10.1007/s13278-021-00823-2
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Rossetti G.
Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark, a model that generates synthetic node-attributed graphs with planted communities. Its novelty consists in forming communities and node labels contextually while handling categorical or continuous attributive information. Moreover, we propose a comparison between attribute-aware algorithms, testing them against our benchmark. Accordingly to different classification schema from recent state-of-the-art surveys, our results suggest that X-Mark can shed light on the differences between several families of algorithms.Source: Social Network Analysis and Mining 11 (2021). doi:10.1007/s13278-021-00823-2
DOI: 10.1007/s13278-021-00823-2
Project(s): SoBigData-PlusPlus
Metrics:
See at:
link.springer.com | Social Network Analysis and Mining | Social Network Analysis and Mining | ISTI Repository | CNR ExploRA
2020
Conference article
Open Access
Eva: attribute-aware network segmentation
Citraro S., Rossetti G.
Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.Source: International Conference on Complex Networks and their Applications, pp. 141–151, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_12
DOI: 10.48550/arxiv.1910.06599
Project(s): SoBigData
Metrics:
Citraro S., Rossetti G.
Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.Source: International Conference on Complex Networks and their Applications, pp. 141–151, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_12
DOI: 10.48550/arxiv.1910.06599
Project(s): SoBigData
Metrics:
See at:
arXiv.org e-Print Archive | arxiv.org | ISTI Repository | doi.org | doi.org | link.springer.com | CNR ExploRA
2020
Report
Unknown
UTLDR: an agent-based framework for modeling infectious diseases and public interventions
Rossetti G., Milli L., Citraro S., Morini V.
Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencing a constantly growing interest from researchers of heterogeneous fields of study. Indeed, the vast literature on computational epidemiology offers solid grounds for analytical studies and the definition of novel models aimed at both predictive and prescriptive scenario descriptions. To ease the access to diffusion modelling, several programming libraries and tools have been proposed during the last decade: however, to the best of our knowledge, none of them is explicitly designed to allow its users to integrate public interventions in their model. In this work, we introduce UTLDR, a framework that can simulate the effects of several public interventions (and their combinations) on the unfolding of epidemic processes. UTLDR enables the design of compartmental models incrementally and to simulate them over complex interaction network topologies. Moreover, it allows integrating external information on the analyzed population (e.g., age, gender, geographical allocation, and mobility patterns. . . ) and to use it to stratify and refine the designed model. After introducing the framework, we provide a few case studies to underline its flexibility and expressive power.Source: ISTI Working Papers, 2020
Project(s): SoBigData-PlusPlus
Rossetti G., Milli L., Citraro S., Morini V.
Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencing a constantly growing interest from researchers of heterogeneous fields of study. Indeed, the vast literature on computational epidemiology offers solid grounds for analytical studies and the definition of novel models aimed at both predictive and prescriptive scenario descriptions. To ease the access to diffusion modelling, several programming libraries and tools have been proposed during the last decade: however, to the best of our knowledge, none of them is explicitly designed to allow its users to integrate public interventions in their model. In this work, we introduce UTLDR, a framework that can simulate the effects of several public interventions (and their combinations) on the unfolding of epidemic processes. UTLDR enables the design of compartmental models incrementally and to simulate them over complex interaction network topologies. Moreover, it allows integrating external information on the analyzed population (e.g., age, gender, geographical allocation, and mobility patterns. . . ) and to use it to stratify and refine the designed model. After introducing the framework, we provide a few case studies to underline its flexibility and expressive power.Source: ISTI Working Papers, 2020
Project(s): SoBigData-PlusPlus
See at: CNR ExploRA
2020
Report
Open Access
Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.Source: ISTI Working Papers, 2020, 2020
Project(s): SoBigData-PlusPlus
Rossetti G., Citraro S., Milli L.
Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.Source: ISTI Working Papers, 2020, 2020
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2020
Journal article
Open Access
Identifying and exploiting homogeneous communities in labeled networks
Citraro S., Rossetti G.
Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting Eva, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate Eva on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that Eva is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better Eva: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.Source: Applied network science 5 (2020). doi:10.1007/s41109-020-00302-1
DOI: 10.1007/s41109-020-00302-1
Project(s): SoBigData-PlusPlus
Metrics:
Citraro S., Rossetti G.
Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting Eva, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate Eva on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that Eva is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better Eva: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.Source: Applied network science 5 (2020). doi:10.1007/s41109-020-00302-1
DOI: 10.1007/s41109-020-00302-1
Project(s): SoBigData-PlusPlus
Metrics:
See at:
appliednetsci.springeropen.com | Applied Network Science | Applied Network Science | Applied Network Science | ISTI Repository | CNR ExploRA
2019
Conference article
Open Access
A complex network approach to semantic spaces: How meaning organizes itself
Citraro S., Rossetti G.
We propose a complex network approach to the emergence of word meaning through the analysis of semantic spaces: NLP techniques able to capture an aspect of meaning based on distributional semantic theories, so that words are linked to each other if they can be substituted in the same linguistic contexts, forming clusters representing semantic fields. This approach can be used to model a mental lexicon of word similarities: a graph G = (N, L) where N are words connected by some type of semantic or associative property L. Networks extracted from a baseline neural language model are analyzed in terms of global properties: they are small world and the probability of degree distribution follows a truncated power law. Moreover, they throw in a strong degree assortativity, a peculiarity that introduces us to the problem of semantic field identification. We support the idea that semantic fields can be identified exploiting the topological information of networks. Several community discovery methods have been tested, identifying from time to time strict semantic fields as crisp communities, linguistic contexts as overlapping communities or meaning conveyed by single words as communities produced starting from a seed-set expansion.Source: Italian Symposium on Advanced Database Systems, Castiglione Della Pescaia (GR), 19-19/6/2019
Project(s): SoBigData
Citraro S., Rossetti G.
We propose a complex network approach to the emergence of word meaning through the analysis of semantic spaces: NLP techniques able to capture an aspect of meaning based on distributional semantic theories, so that words are linked to each other if they can be substituted in the same linguistic contexts, forming clusters representing semantic fields. This approach can be used to model a mental lexicon of word similarities: a graph G = (N, L) where N are words connected by some type of semantic or associative property L. Networks extracted from a baseline neural language model are analyzed in terms of global properties: they are small world and the probability of degree distribution follows a truncated power law. Moreover, they throw in a strong degree assortativity, a peculiarity that introduces us to the problem of semantic field identification. We support the idea that semantic fields can be identified exploiting the topological information of networks. Several community discovery methods have been tested, identifying from time to time strict semantic fields as crisp communities, linguistic contexts as overlapping communities or meaning conveyed by single words as communities produced starting from a seed-set expansion.Source: Italian Symposium on Advanced Database Systems, Castiglione Della Pescaia (GR), 19-19/6/2019
Project(s): SoBigData
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA