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.Source: Temporal Network Theory, edited by Holme P.; Saramäki J., pp. 181–197, 2019
DOI: 10.1007/978-3-030-23495-9_10
Metrics:
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.Source: Temporal Network Theory, edited by Holme P.; Saramäki J., pp. 181–197, 2019
DOI: 10.1007/978-3-030-23495-9_10
Metrics:
See at:
arxiv.org 
| link.springer.com | ISTI Repository | doi.org 
| HAL-ENS-LYON | CNR ExploRA
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 performancesSource: ICDMW 2016 - IEEE 16th International Conference on Data Mining Workshops, pp. 152–159, Barcellona, Spain, 12-15 December 2016
DOI: 10.1109/icdmw.2016.0029
Project(s): SoBigData
Metrics:
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 performancesSource: ICDMW 2016 - IEEE 16th International Conference on Data Mining Workshops, pp. 152–159, Barcellona, Spain, 12-15 December 2016
DOI: 10.1109/icdmw.2016.0029
Project(s): SoBigData
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
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 ExploRA
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.Source: International European Conference on Parallel and Distributed Computing (Euro-Par), LSDVE Workshop, 8/2/2018
DOI: 10.1007/978-3-319-75178-8_42
Metrics:
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.Source: International European Conference on Parallel and Distributed Computing (Euro-Par), LSDVE Workshop, 8/2/2018
DOI: 10.1007/978-3-319-75178-8_42
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
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) 5 (2017): 893–912. doi:10.1093/comnet/cnx016
DOI: 10.1093/comnet/cnx016
Project(s): CIMPLEX, SoBigData
Metrics:
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) 5 (2017): 893–912. doi:10.1093/comnet/cnx016
DOI: 10.1093/comnet/cnx016
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
ISTI Repository | academic.oup.com | Journal of Complex Networks | CNR ExploRA
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 51 (2018). doi:10.1145/3172867
DOI: 10.1145/3172867
DOI: 10.48550/arxiv.1707.03186
Metrics:
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 51 (2018). doi:10.1145/3172867
DOI: 10.1145/3172867
DOI: 10.48550/arxiv.1707.03186
Metrics:
See at:
arXiv.org e-Print Archive | ACM Computing Surveys | ISTI Repository | dl.acm.org | ACM Computing Surveys | doi.org | HAL-ENS-LYON | CNR ExploRA
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: International Conference on Complex Networks and their Applications, pp. 152–163, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_13
Project(s): SoBigData
Metrics:
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: International Conference on Complex Networks and their Applications, pp. 152–163, Lisbon, Portugal, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_13
Project(s): SoBigData
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
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 17 (2019): 23–44. doi:10.1007/s10723-018-9448-0
DOI: 10.1007/s10723-018-9448-0
Metrics:
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 17 (2019): 23–44. doi:10.1007/s10723-018-9448-0
DOI: 10.1007/s10723-018-9448-0
Metrics:
See at:
ISTI Repository | Journal of Grid Computing | link.springer.com | CNR ExploRA
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 5 (2020). doi:10.1007/s41109-020-00270-6
DOI: 10.1007/s41109-020-00270-6
Project(s): SoBigData-PlusPlus
Metrics:
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 5 (2020). doi:10.1007/s41109-020-00270-6
DOI: 10.1007/s41109-020-00270-6
Project(s): SoBigData-PlusPlus
Metrics:
See at:
Applied Network Science | Applied Network Science | Applied Network Science | ISTI Repository | CNR ExploRA
2015
Doctoral thesis
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 | ISTI Repository | CNR ExploRA
2020
Report
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.Source: ISTI Technical Reports 2020/013, 2020
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.Source: ISTI Technical Reports 2020/013, 2020
DOI: 10.32079/isti-tr-2020/013
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | CNR ExploRA
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) 8 (2020). doi:10.1093/comnet/cnaa027
DOI: 10.1093/comnet/cnaa027
DOI: 10.48550/arxiv.2007.08635
Project(s): BITUNAM, SoBigData-PlusPlus
Metrics:
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) 8 (2020). doi:10.1093/comnet/cnaa027
DOI: 10.1093/comnet/cnaa027
DOI: 10.48550/arxiv.2007.08635
Project(s): BITUNAM
, SoBigData-PlusPlus Metrics:
See at:
academic.oup.com | arXiv.org e-Print Archive | Journal of Complex Networks | Journal of Complex Networks | ISTI Repository | doi.org | Hyper Article en Ligne | CNR ExploRA
2021
Contribution to conference
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.Source: Complex Networks 2020 - The 9th International Conference on Complex Networks and their Applications, Online conference, 1-3/12/2020
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.Source: Complex Networks 2020 - The 9th International Conference on Complex Networks and their Applications, Online conference, 1-3/12/2020
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | ISTI Repository | CNR ExploRA
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: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 744–755, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93409-5_61
Metrics:
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: COMPLEX NETWORKS 2021 - Tenth International Conference on Complex Networks and Their Applications, pp. 744–755, Madrid, Spain, 30/11-2/12/2021
DOI: 10.1007/978-3-030-93409-5_61
Metrics:
See at:
link.springer.com | ISTI Repository | CNR ExploRA
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 (2022). doi:10.1007/s41109-022-00482-y
DOI: 10.1007/s41109-022-00482-y
DOI: 10.17863/cam.85951
DOI: 10.48550/arxiv.2202.12859
Project(s): SoBigData-PlusPlus
Metrics:
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 (2022). doi:10.1007/s41109-022-00482-y
DOI: 10.1007/s41109-022-00482-y
DOI: 10.17863/cam.85951
DOI: 10.48550/arxiv.2202.12859
Project(s): SoBigData-PlusPlus
Metrics:
See at:
appliednetsci.springeropen.com | arXiv.org e-Print Archive | Applied Network Science | ISTI Repository | Apollo | doi.org | Apollo | CNR ExploRA
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: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 86–97, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_8
Project(s): SoBigData-PlusPlus
Metrics:
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: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 86–97, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_8
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2023
Conference article
Open Access
Attributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit
Failla A., Citraro S., Rossetti G.
Complex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups. Yet even such higher-order representations ignore the importance of the rich attributes carried by the nodes. In this work we introduce ASH, an Attributed Stream-Hypernetwork framework to model higher-order temporal networks with attributes on nodes. We leverage ASH to study pairwise and group political discussions on the well-known Reddit platform. Our analysis unveils different patterns while looking at either a pairwise or a higher-order structure for the same phenomena. In particular, we find out that Reddit users tend to surround themselves by like-minded peers with respect to their political leaning when online discussions are proxied by pairwise interactions; conversely, such a tendency significantly decreases when considering nodes embedded in higher-order contexts - that often describe heterophilic discussions.Source: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 150–161, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_13
Project(s): SoBigData-PlusPlus
Metrics:
Failla A., Citraro S., Rossetti G.
Complex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups. Yet even such higher-order representations ignore the importance of the rich attributes carried by the nodes. In this work we introduce ASH, an Attributed Stream-Hypernetwork framework to model higher-order temporal networks with attributes on nodes. We leverage ASH to study pairwise and group political discussions on the well-known Reddit platform. Our analysis unveils different patterns while looking at either a pairwise or a higher-order structure for the same phenomena. In particular, we find out that Reddit users tend to surround themselves by like-minded peers with respect to their political leaning when online discussions are proxied by pairwise interactions; conversely, such a tendency significantly decreases when considering nodes embedded in higher-order contexts - that often describe heterophilic discussions.Source: COMPLEX NETWORKS 2022 - Eleventh International Conference on Complex Networks and Their Applications, pp. 150–161, Palermo, Italy, 08-10/11/2022
DOI: 10.1007/978-3-031-21127-0_13
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2023
Journal article
Open Access
Advanced analysis technologies for social media
Guidi B., Iglesias C. A., 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 13 (2023). doi:10.3390/app13031909
DOI: 10.3390/app13031909
Metrics:
Guidi B., Iglesias C. A., 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 13 (2023). doi:10.3390/app13031909
DOI: 10.3390/app13031909
Metrics:
See at:
Applied Sciences | ISTI Repository | www.mdpi.com | CNR ExploRA
2013
Conference article
Unknown
Measuring tie strength in multidimensional networks
Rossetti G., Pappalardo L., Pedreschi D.
Online social networks have allowed us to build massive net- works of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are people we really trust, persons most like us and whose social circles tightly overlap with our own. Unfortunately, social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 223–230, Rocella Jonica, Italy, June 30 - July 03 2013
Project(s): DATA SIM
Rossetti G., Pappalardo L., Pedreschi D.
Online social networks have allowed us to build massive net- works of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are people we really trust, persons most like us and whose social circles tightly overlap with our own. Unfortunately, social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.Source: SEBD 2013 - 21st Italian Symposium on Advanced Database Systems, pp. 223–230, Rocella Jonica, Italy, June 30 - July 03 2013
Project(s): DATA SIM
See at: CNR ExploRA
2014
Journal article
Restricted
Uncovering hierarchical and overlapping communities with a local-first approach
Coscia M., Rossetti G., Pedreschi D., Giannotti F.
Community discovery in complex networks is the task of organizing a network's structure by grouping together nodes related to each other. Traditional approaches are based on the assumption that there is a global-level organization in the network. However, in many scenarios, each node is the bearer of complex information and cannot be classified in disjoint clusters. The top-down global view of the partition approach is not designed for this. Here, we represent this complex information as multiple latent labels, and we postulate that edges in the networks are created among nodes carrying similar labels. The latent labels are the communities a node belongs to and we discover them with a simple local-first approach to community discovery. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, its ego neighborhood, using a label propagation algorithm, assuming that each node is aware of the label it shares with each of its connections. The local communities are merged hierarchically, unveiling the modular organization of the network at the global level and identifying overlapping groups and groups of groups. We tested this intuition against the state-of-the-art overlapping community discovery and found that our new method advances in the chosen scenarios in the quality of the obtained communities. We perform a test on benchmark and on real-world networks, evaluating the quality of the community coverage by using the extracted communities to predict the metadata attached to the nodes, which we consider external information about the latent labels. We also provide an explanation about why real-world networks contain overlapping communities and how our logic is able to capture them. Finally, we show how our method is deterministic, is incremental, and has a limited time complexity, so that it can be used on real-world scale networks.Source: ACM transactions on knowledge discovery from data (Online) 9 (2014): 6–27. doi:10.1145/2629511
DOI: 10.1145/2629511
Project(s): DATA SIM, ICES: Small: Using Web Crawling and Complex Network Analysis to Understand Institutional Activity and Connectivity
Metrics:
Coscia M., Rossetti G., Pedreschi D., Giannotti F.
Community discovery in complex networks is the task of organizing a network's structure by grouping together nodes related to each other. Traditional approaches are based on the assumption that there is a global-level organization in the network. However, in many scenarios, each node is the bearer of complex information and cannot be classified in disjoint clusters. The top-down global view of the partition approach is not designed for this. Here, we represent this complex information as multiple latent labels, and we postulate that edges in the networks are created among nodes carrying similar labels. The latent labels are the communities a node belongs to and we discover them with a simple local-first approach to community discovery. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, its ego neighborhood, using a label propagation algorithm, assuming that each node is aware of the label it shares with each of its connections. The local communities are merged hierarchically, unveiling the modular organization of the network at the global level and identifying overlapping groups and groups of groups. We tested this intuition against the state-of-the-art overlapping community discovery and found that our new method advances in the chosen scenarios in the quality of the obtained communities. We perform a test on benchmark and on real-world networks, evaluating the quality of the community coverage by using the extracted communities to predict the metadata attached to the nodes, which we consider external information about the latent labels. We also provide an explanation about why real-world networks contain overlapping communities and how our logic is able to capture them. Finally, we show how our method is deterministic, is incremental, and has a limited time complexity, so that it can be used on real-world scale networks.Source: ACM transactions on knowledge discovery from data (Online) 9 (2014): 6–27. doi:10.1145/2629511
DOI: 10.1145/2629511
Project(s): DATA SIM
, ICES: Small: Using Web Crawling and Complex Network Analysis to Understand Institutional Activity and Connectivity Metrics:
See at:
ACM Transactions on Knowledge Discovery from Data | www.scopus.com | CNR ExploRA