2019
Contribution to book
Open Access
Challenges in community discovery on temporal networks
Cazabet R, Rossetti G
Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.
Cazabet R, Rossetti G
Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.
See at:
CNR IRIS 
| link.springer.com | ISTI Repository | CNR IRIS 
2016
Conference article
Restricted
Football market strategies: think locally, trade globally
Rossetti G, Caproni V
Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents? Does it pay to constantly change the club roster? In this work we identify archetypal market strategies over 25 years of transfer market as depicted by UEFA professional clubs and study their impact on sportive success. Our analysis underline how, regardless from clubs' available budgets, transfer market strategies deeply impact - on the long run - football sportive performancesProject(s): SoBigData
Rossetti G, Caproni V
Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents? Does it pay to constantly change the club roster? In this work we identify archetypal market strategies over 25 years of transfer market as depicted by UEFA professional clubs and study their impact on sportive success. Our analysis underline how, regardless from clubs' available budgets, transfer market strategies deeply impact - on the long run - football sportive performancesProject(s): SoBigData
See at:
CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2017
Contribution to book
Open Access
Dynamic Community Detection
Cazabet R, Rossetti G, Amblard F
Community detection is one of the most popular topics in the field of network analysis. Since the seminal paper of Girvan and Newman (2002), hundreds of papers have been published on the topic. From the initial problem of graph partitioning, in which each node of the network must belong to one and only one community, new aspects of community structures have been taken into consideration, such as overlapping communities and hierarchical decomposition. Recently, new methods have been proposed, which can handle dynamic networks. The communities found by these algorithms are called dynamic communities.DOI: 10.1007/978-1-4614-7163-9_383-1
DOI: 10.1007/978-1-4939-7131-2_383
Metrics:
Cazabet R, Rossetti G, Amblard F
Community detection is one of the most popular topics in the field of network analysis. Since the seminal paper of Girvan and Newman (2002), hundreds of papers have been published on the topic. From the initial problem of graph partitioning, in which each node of the network must belong to one and only one community, new aspects of community structures have been taken into consideration, such as overlapping communities and hierarchical decomposition. Recently, new methods have been proposed, which can handle dynamic networks. The communities found by these algorithms are called dynamic communities.DOI: 10.1007/978-1-4614-7163-9_383-1
DOI: 10.1007/978-1-4939-7131-2_383
Metrics:
See at:
hal.archives-ouvertes.fr | doi.org | doi.org | Hyper Article en Ligne | CNR IRIS | CNR IRIS
2017
Conference article
Restricted
Dynamic Community Analysis in Decentralized Online Social Networks
Guidi B, Michienzi A, Rossetti G
Community structure is one of the most studied features of Online Social Networks (OSNs). Community detection guarantees sev- eral advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. One of the main challenge in DOSNs concerns the availability of social data and communities can be exploited to guarantee a more efficient solution about the data availabil- ity problem. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic social networks, such as DOSNs, where the online/offline status of user changes very frequently. In this paper, we focus our attention on a preliminary analysis of dynamic community detection in DOSNs by studying a real Facebook dataset to evaluate how frequent the communities change over time and which events are more frequent. The results prove that the so- cial graph has a high instability and distributed solutions to manage the dynamism are needed.
Guidi B, Michienzi A, Rossetti G
Community structure is one of the most studied features of Online Social Networks (OSNs). Community detection guarantees sev- eral advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. One of the main challenge in DOSNs concerns the availability of social data and communities can be exploited to guarantee a more efficient solution about the data availabil- ity problem. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic social networks, such as DOSNs, where the online/offline status of user changes very frequently. In this paper, we focus our attention on a preliminary analysis of dynamic community detection in DOSNs by studying a real Facebook dataset to evaluate how frequent the communities change over time and which events are more frequent. The results prove that the so- cial graph has a high instability and distributed solutions to manage the dynamism are needed.
See at:
CNR IRIS | CNR IRIS | link.springer.com
2017
Journal article
Open Access
RDyn: graph benchmark handling community dynamics
Rossetti G
Graph models provide an understanding of the dynamics of network formation and evolution; as a direct consequence, synthesizing graphs having controlled topology and planted partitions has been often identified as a strategy to describe benchmarks able to assess the performances of community discovery algorithm. However, one relevant aspect of real-world networks has been ignored by benchmarks proposed so far: community dynamics. As time goes by network communities rise, fall and may interact with each other generating merges and splits. Indeed, during the last decade dynamic community discovery has become a very active research field: in order to provide a coherent environment to test novel algorithms aimed at identifying mutable network partitions we introduce RDYN, an approach able to generates dynamic networks along with time-dependent ground-truth partitions having tunable quality.Source: JOURNAL OF COMPLEX NETWORKS (ONLINE), vol. 5 (issue 6), pp. 893-912
Project(s): CIMPLEX, SoBigData
Rossetti G
Graph models provide an understanding of the dynamics of network formation and evolution; as a direct consequence, synthesizing graphs having controlled topology and planted partitions has been often identified as a strategy to describe benchmarks able to assess the performances of community discovery algorithm. However, one relevant aspect of real-world networks has been ignored by benchmarks proposed so far: community dynamics. As time goes by network communities rise, fall and may interact with each other generating merges and splits. Indeed, during the last decade dynamic community discovery has become a very active research field: in order to provide a coherent environment to test novel algorithms aimed at identifying mutable network partitions we introduce RDYN, an approach able to generates dynamic networks along with time-dependent ground-truth partitions having tunable quality.Source: JOURNAL OF COMPLEX NETWORKS (ONLINE), vol. 5 (issue 6), pp. 893-912
Project(s): CIMPLEX
, SoBigData
See at:
academic.oup.com | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2018
Journal article
Open Access
Community discovery in dynamic networks: A survey
Rossetti G, Cazabet R
Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a "user manual," this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.Source: ACM COMPUTING SURVEYS, vol. 51 (issue 2)
Rossetti G, Cazabet R
Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a "user manual," this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.Source: ACM COMPUTING SURVEYS, vol. 51 (issue 2)
See at:
dl.acm.org | CNR IRIS | ISTI Repository | CNR IRIS | CNR IRIS
2018
Book
Open Access
OSNED 2018 Chairs' Welcome & Organization
Cazabet R, Passarella A, Rossetti G, Silvestri F
It is our great pleasure to welcome you to the WWW 2018 OSNED workshop (Online Social Networks and Media: Network Properties and Dynamics). Online Social Networks and Media (OSNEM) are one of the most disruptive communication platforms of the last 15 years with high socio-economic value. Within this framework, the network properties of OSNEM can be used to capture multiple phenomena related to OSNEM, at different logical layers, from a technical perspective (e.g., OSNEM data management and information diffusion), as well as a societal perspective (e.g., the OSNEM users' social structures). Moreover, the analysis of network dynamics represents one of the biggest challenges that emerged in recent years within the network science community.
Cazabet R, Passarella A, Rossetti G, Silvestri F
It is our great pleasure to welcome you to the WWW 2018 OSNED workshop (Online Social Networks and Media: Network Properties and Dynamics). Online Social Networks and Media (OSNEM) are one of the most disruptive communication platforms of the last 15 years with high socio-economic value. Within this framework, the network properties of OSNEM can be used to capture multiple phenomena related to OSNEM, at different logical layers, from a technical perspective (e.g., OSNEM data management and information diffusion), as well as a societal perspective (e.g., the OSNEM users' social structures). Moreover, the analysis of network dynamics represents one of the biggest challenges that emerged in recent years within the network science community.
See at:
CNR IRIS | ISTI Repository | osned.netsons.org | CNR IRIS
2020
Conference article
Open Access
Exorcising the demon: angel, efficient node-centric community discovery
Rossetti G
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (PRINT), pp. 152-163. Lisbon, Portugal, 10-12/12/2019
Project(s): SoBigData
Rossetti G
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (PRINT), pp. 152-163. Lisbon, Portugal, 10-12/12/2019
Project(s): SoBigData
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2019
Journal article
Open Access
Towards the Dynamic Community Discovery in Decentralized Online Social Networks
Guidi B, Michienzi A, Rossetti G
The community structure is one of the most studied features of the Online Social Networks (OSNs). Community detection guarantees several advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. Several challenges in DOSNs can be faced by exploiting communities. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic environments, where churn is a real problem. In this paper, we focus our attention on the analysis of dynamic community detection in DOSNs by studying a real Facebook dataset. We evaluate two different dynamic community discovery classes to understand which of them can be applied to a distributed environment. Results prove that the social graph has high instability and distributed solutions to manage the dynamism are needed and show that a Temporal Trade-off class is the most promising one.Source: JOURNAL OF GRID COMPUTING, vol. 17 (issue 1), pp. 23-44
Guidi B, Michienzi A, Rossetti G
The community structure is one of the most studied features of the Online Social Networks (OSNs). Community detection guarantees several advantages for both centralized and decentralized social networks. Decentralized Online Social Networks (DOSNs) have been proposed to provide more control over private data. Several challenges in DOSNs can be faced by exploiting communities. The detection of communities and the management of their evolution represents a hard process, especially in highly dynamic environments, where churn is a real problem. In this paper, we focus our attention on the analysis of dynamic community detection in DOSNs by studying a real Facebook dataset. We evaluate two different dynamic community discovery classes to understand which of them can be applied to a distributed environment. Results prove that the social graph has high instability and distributed solutions to manage the dynamism are needed and show that a Temporal Trade-off class is the most promising one.Source: JOURNAL OF GRID COMPUTING, vol. 17 (issue 1), pp. 23-44
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
2019
Journal article
Restricted
Editorial - Special issue on OSNEM network properties and dynamics
Cazabet R, Passarella A, Rossetti G, Silvestri F
Source: ONLINE SOCIAL NETWORKS AND MEDIA, vol. 12, p. 21
Cazabet R, Passarella A, Rossetti G, Silvestri F
Source: ONLINE SOCIAL NETWORKS AND MEDIA, vol. 12, p. 21
See at:
CNR IRIS | CNR IRIS | www.sciencedirect.com
2020
Journal article
Open Access
ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks
Rossetti G
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: APPLIED NETWORK SCIENCE, vol. 5 (issue 1)
Rossetti G
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: APPLIED NETWORK SCIENCE, vol. 5 (issue 1)
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS
2015
Other
Open Access
Social Network Dynamics
Rossetti G
This thesis focuses on the analysis of structural and topological network problems. In particular, in this work the privileged subjects of investigation will be both static and dynamic social networks. Nowadays, the constantly growing availability of Big Data describing human behaviors (i.e., the ones provided by online social networks, telco companies, insurances, airline companies. . . ) offers the chance to evaluate and validate, on large scale realities, the performances of algorithmic approaches and the soundness of sociological theories. In this scenario, exploiting data-driven methodologies enables for a more careful modeling and thorough understanding of observed phenomena. In the last decade, graph theory has lived a second youth: the scientific community has extensively adopted, and sharpened, its tools to shape the so called Network Science. Within this highly active field of research, it is recently emerged the need to extend classic network analytical methodologies in order to cope with a very important, previously underestimated, semantic information: time. Such awareness has been the linchpin for recent works that have started to redefine form scratch well known network problems in order to better understand the evolving nature of human interactions. Indeed, social networks are highly dynamic realities: nodes and edges appear and disappear as time goes by describing the natural lives of social ties: for this reason. it is mandatory to assess the impact that time-aware approaches have on the solution of network problems. Moving from the analysis of the strength of social ties, passing through node ranking and link prediction till reaching community discovery, this thesis aims to discuss data-driven methodologies specifically tailored to approach social network issues in semantic enriched scenarios. To this end, both static and dynamic analytical processes will be introduced and tested on real world data.
Rossetti G
This thesis focuses on the analysis of structural and topological network problems. In particular, in this work the privileged subjects of investigation will be both static and dynamic social networks. Nowadays, the constantly growing availability of Big Data describing human behaviors (i.e., the ones provided by online social networks, telco companies, insurances, airline companies. . . ) offers the chance to evaluate and validate, on large scale realities, the performances of algorithmic approaches and the soundness of sociological theories. In this scenario, exploiting data-driven methodologies enables for a more careful modeling and thorough understanding of observed phenomena. In the last decade, graph theory has lived a second youth: the scientific community has extensively adopted, and sharpened, its tools to shape the so called Network Science. Within this highly active field of research, it is recently emerged the need to extend classic network analytical methodologies in order to cope with a very important, previously underestimated, semantic information: time. Such awareness has been the linchpin for recent works that have started to redefine form scratch well known network problems in order to better understand the evolving nature of human interactions. Indeed, social networks are highly dynamic realities: nodes and edges appear and disappear as time goes by describing the natural lives of social ties: for this reason. it is mandatory to assess the impact that time-aware approaches have on the solution of network problems. Moving from the analysis of the strength of social ties, passing through node ranking and link prediction till reaching community discovery, this thesis aims to discuss data-driven methodologies specifically tailored to approach social network issues in semantic enriched scenarios. To this end, both static and dynamic analytical processes will be introduced and tested on real world data.
See at:
etd.adm.unipi.it | CNR IRIS | ISTI Repository | CNR IRIS
2020
Other
Open Access
Evaluating community detection algorithms for progressively evolving graphs
Cazabet R, Boudebza S, Rossetti G
Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification, and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.DOI: 10.32079/isti-tr-2020/013
Project(s): SoBigData-PlusPlus
Metrics:
Cazabet R, Boudebza S, Rossetti G
Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification, and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.DOI: 10.32079/isti-tr-2020/013
Project(s): SoBigData-PlusPlus
Metrics:
See at:
CNR IRIS | ISTI Repository | CNR IRIS
2020
Journal article
Open Access
Evaluating community detection algorithms for progressively evolving graphs
Cazabet R, Boudebza S, Rossetti G
Many algorithms have been proposed in the last 10 years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.Source: JOURNAL OF COMPLEX NETWORKS (PRINT), vol. 8 (issue 6)
Cazabet R, Boudebza S, Rossetti G
Many algorithms have been proposed in the last 10 years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.Source: JOURNAL OF COMPLEX NETWORKS (PRINT), vol. 8 (issue 6)
See at:
academic.oup.com | CNR IRIS | ISTI Repository | CNR IRIS
2021
Conference article
Open Access
Sockpuppet detection: a Telegram case study
Pisciotta G, Somenzi M, Barisani E, Rossetti G
In Online Social Networks (OSN) numerous are the cases in which users create multiple accounts that publicly seem to belong to different people but are actually fake identities of the same person. These fictitious characters can be exploited to carry out abusive behaviors such as manipulating opinions, spreading fake news and disturbing other users. In literature this problem is known as the Sockpuppet problem. In our work we focus on Telegram, a wide-spread instant messaging application, often known for its exploitation by members of organized crime and terrorism, and more in general for its high presence of people who have offensive behaviors.Project(s): SoBigData-PlusPlus
Pisciotta G, Somenzi M, Barisani E, Rossetti G
In Online Social Networks (OSN) numerous are the cases in which users create multiple accounts that publicly seem to belong to different people but are actually fake identities of the same person. These fictitious characters can be exploited to carry out abusive behaviors such as manipulating opinions, spreading fake news and disturbing other users. In literature this problem is known as the Sockpuppet problem. In our work we focus on Telegram, a wide-spread instant messaging application, often known for its exploitation by members of organized crime and terrorism, and more in general for its high presence of people who have offensive behaviors.Project(s): SoBigData-PlusPlus
See at:
arxiv.org | CNR IRIS | ISTI Repository | ISTI Repository | CNR IRIS | CNR IRIS
2022
Conference article
Open Access
Can you always reap what you sow? Network and functional data analysis of venture capital investments in health-tech companies
Esposito C, Gortan M, Testa L, Chiaromonte F, Fagiolo G, Mina A, Rossetti G
"Success" of firms in venture capital markets is hard to define, and its determinants are still poorly understood. We build a bipartite network of investors and firms in the healthcare sector, describing its structure and its communities. Then, we characterize "success" by introducing progressively more refined definitions, and we find a positive association between such definitions and the centrality of a company. In particular, we are able to cluster funding trajectories of firms into two groups capturing different "success" regimes and to link the probability of belonging to one or the other to their network features (in particular their centrality and the one of their investors). We further investigate this positive association by introducing scalar as well as functional "success" outcomes, confirming our findings and their robustness.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 744-755. Madrid, Spain, 30/11-2/12/2021
Esposito C, Gortan M, Testa L, Chiaromonte F, Fagiolo G, Mina A, Rossetti G
"Success" of firms in venture capital markets is hard to define, and its determinants are still poorly understood. We build a bipartite network of investors and firms in the healthcare sector, describing its structure and its communities. Then, we characterize "success" by introducing progressively more refined definitions, and we find a positive association between such definitions and the centrality of a company. In particular, we are able to cluster funding trajectories of firms into two groups capturing different "success" regimes and to link the probability of belonging to one or the other to their network features (in particular their centrality and the one of their investors). We further investigate this positive association by introducing scalar as well as functional "success" outcomes, confirming our findings and their robustness.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 744-755. Madrid, Spain, 30/11-2/12/2021
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS
2022
Journal article
Open Access
Venture capital investments through the lens of network and functional data analysis
Esposito C., Gortan M., Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G.
In this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms' and investors' relations. Focusing on deal-level data for the health sector, we first create a bipartite network among firms and investors, and then apply functional data analysis to derive progressively more refined indicators of success captured by a binary, a scalar and a functional outcome. More specifically, we use different network centrality measures to capture the role of early investments for the success of the firm. Our results, which are robust to different specifications, suggest that success has a strong positive association with centrality measures of the firm and of its large investors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowledge bridges. Finally, based on our analyses, success is not associated with firms' and investors' spreading power (harmonic centrality), nor with the tightness of investors' community (clustering coefficient) and spreading ability (VoteRank).Source: APPLIED NETWORK SCIENCE
Project(s): SoBigData-PlusPlus
Esposito C., Gortan M., Testa L., Chiaromonte F., Fagiolo G., Mina A., Rossetti G.
In this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms' and investors' relations. Focusing on deal-level data for the health sector, we first create a bipartite network among firms and investors, and then apply functional data analysis to derive progressively more refined indicators of success captured by a binary, a scalar and a functional outcome. More specifically, we use different network centrality measures to capture the role of early investments for the success of the firm. Our results, which are robust to different specifications, suggest that success has a strong positive association with centrality measures of the firm and of its large investors, and a weaker but still detectable association with centrality measures of small investors and features describing firms as knowledge bridges. Finally, based on our analyses, success is not associated with firms' and investors' spreading power (harmonic centrality), nor with the tightness of investors' community (clustering coefficient) and spreading ability (VoteRank).Source: APPLIED NETWORK SCIENCE
Project(s): SoBigData-PlusPlus
See at:
appliednetsci.springeropen.com | CNR IRIS | ISTI Repository | CNR IRIS
2023
Conference article
Open Access
Change my mind: data driven estimate of open-mindedness from political discussions
Pansanella V, Morini V, Squartini T, Rossetti G
One of the main dimensions characterizing the unfolding of opinion formation processes in social debates is the degree of open-mindedness of the involved population. Opinion dynamic modeling studies have tried to capture such a peculiar expression of individuals' personalities and relate it to emerging phenomena like polarization, radicalization, and ideology fragmentation. However, one of their major limitations lies in the strong assumptions they make on the initial distribution of such characteristics, often fixed so as to satisfy a normality hypothesis. Here we propose a data-driven methodology to estimate users' open-mindedness from online discussion data. Our analysis--focused on the political discussion taking place on Reddit during the first two years of the Trump presidency--unveils the existence of statistically diverse distributions of open-mindedness in annotated sub-populations (i.e., Republicans, Democrats, and Moderates/Neutrals). Moreover, such distributions appear to be stable across time and generated by individual users' behaviors that remain consistent and underdispersed.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 86-97. Palermo, Italy, 08-10/11/2022
Project(s): SoBigData-PlusPlus
Pansanella V, Morini V, Squartini T, Rossetti G
One of the main dimensions characterizing the unfolding of opinion formation processes in social debates is the degree of open-mindedness of the involved population. Opinion dynamic modeling studies have tried to capture such a peculiar expression of individuals' personalities and relate it to emerging phenomena like polarization, radicalization, and ideology fragmentation. However, one of their major limitations lies in the strong assumptions they make on the initial distribution of such characteristics, often fixed so as to satisfy a normality hypothesis. Here we propose a data-driven methodology to estimate users' open-mindedness from online discussion data. Our analysis--focused on the political discussion taking place on Reddit during the first two years of the Trump presidency--unveils the existence of statistically diverse distributions of open-mindedness in annotated sub-populations (i.e., Republicans, Democrats, and Moderates/Neutrals). Moreover, such distributions appear to be stable across time and generated by individual users' behaviors that remain consistent and underdispersed.Source: STUDIES IN COMPUTATIONAL INTELLIGENCE (INTERNET), pp. 86-97. Palermo, Italy, 08-10/11/2022
Project(s): SoBigData-PlusPlus
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2023
Journal article
Open Access
Advanced analysis technologies for social media
Guidi B, Iglesias Ca, Rossetti G, Koidl K
Interest in social media has only increased with time. Social media today represent the main channel to communicate and share personal information. Social media analysis usually combines content-based and network-based analysis. While content-based approaches analyze media using media analysis techniques, network-based approaches analyze static and dynamic network properties with the aim of detecting influencers for marketing purposes. The network-based analysis represents a fundamental process in order to understand the dynamics of these platforms. New techniques and technologies have been proposed in order to enrich the social media analytics field. In particular, decentralized approaches have been proposed in order to face privacy issues, and AI has been applied in order to improve analysis over large sets of data. The main goal of this Special Issue is to collect research contributions, applications, analyses, methodologies, or strategies that strengthen or face the knowledge of social media thanks to advanced analyses or new technologies, such as P2P networks or blockchain. In detail, 5 papers have been published in the Special Issue out of a total of 10 submitted. The next sections provide a brief summary of each of the papers published.Source: APPLIED SCIENCES, vol. 13 (issue 3)
Guidi B, Iglesias Ca, Rossetti G, Koidl K
Interest in social media has only increased with time. Social media today represent the main channel to communicate and share personal information. Social media analysis usually combines content-based and network-based analysis. While content-based approaches analyze media using media analysis techniques, network-based approaches analyze static and dynamic network properties with the aim of detecting influencers for marketing purposes. The network-based analysis represents a fundamental process in order to understand the dynamics of these platforms. New techniques and technologies have been proposed in order to enrich the social media analytics field. In particular, decentralized approaches have been proposed in order to face privacy issues, and AI has been applied in order to improve analysis over large sets of data. The main goal of this Special Issue is to collect research contributions, applications, analyses, methodologies, or strategies that strengthen or face the knowledge of social media thanks to advanced analyses or new technologies, such as P2P networks or blockchain. In detail, 5 papers have been published in the Special Issue out of a total of 10 submitted. The next sections provide a brief summary of each of the papers published.Source: APPLIED SCIENCES, vol. 13 (issue 3)
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2024
Conference article
Open Access
LLM-generated word association norms
Abramski K., Lavorati C., Rossetti G., Stella M.
Word associations have been extensively used in psychology to study the rich structure of human conceptual knowledge. Recently, the study of word associations has been extended to investigating the knowledge encoded in LLMs. However, because of how the LLM word associations are accessed, existing approaches have been limited in the types of comparisons that can be made between humans and LLMs. To overcome this, we create LLM-generated word association norms modeled after the Small World of Words (SWOW) human-generated word association norms consisting of over 12,000 cue words. We prompt the language models with the same cues and participant profiles as those in the SWOW human-generated norms, and we conduct preliminary comparative analyses between humans and LLMs that explore differences in response variability, biases, concreteness effects, and network properties. Our exploration provides insights into how LLM-generated word associations can be used to investigate similarities and differences in how humans and LLMs process information.Source: FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS, vol. 386, pp. 3-12. MalmoĢ, Sweden, 10-14/06/2024
Abramski K., Lavorati C., Rossetti G., Stella M.
Word associations have been extensively used in psychology to study the rich structure of human conceptual knowledge. Recently, the study of word associations has been extended to investigating the knowledge encoded in LLMs. However, because of how the LLM word associations are accessed, existing approaches have been limited in the types of comparisons that can be made between humans and LLMs. To overcome this, we create LLM-generated word association norms modeled after the Small World of Words (SWOW) human-generated word association norms consisting of over 12,000 cue words. We prompt the language models with the same cues and participant profiles as those in the SWOW human-generated norms, and we conduct preliminary comparative analyses between humans and LLMs that explore differences in response variability, biases, concreteness effects, and network properties. Our exploration provides insights into how LLM-generated word associations can be used to investigate similarities and differences in how humans and LLMs process information.Source: FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS, vol. 386, pp. 3-12. MalmoĢ, Sweden, 10-14/06/2024
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