Superspreaders and superblockers based community evolution tracking in dynamic social networks

Xu, Zhiou; Rui, Xiaobin; He, Jing; Wang, Zhixiao; Hadzibeganovic, Tarik

doi:10.1016/j.knosys.2019.105377

Cited by 22 publications

(14 citation statements)

References 46 publications

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“…4) Our method is only suitable for global non-overlapping community detection, however the community structures in complex networks are diverse. Therefore, we need to absorb some dynamic [22], overlapping [36] and artificial intelligence [62] concepts into our theoretical system in the follow-up research.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…For example, Wang et al [21] believed that communities in social networks are not only overlapping but also evolving, so community evolution must be tracked. Xu et al [22] quantified the changes in dynamic communities and studied a method to detect dynamic communities and identify key evolutionary events. Jin et al [23] considered that semantic communities are important for understanding the function of social networks; however, identifying these communities requires the combination of network topologies and contents.…”

Section: A Community Community Structure and Community Detectionmentioning

confidence: 99%

“…In addition, there are some more sophisticated definitions based on this comparison in local and dynamic community detection [17], [21], e.g., definitions based on topology potential [22], [39]. However, the complexity of solving the optimal solution of this kind of community partition is very high [20], [40].…”

Section: B Community Definitions In the Literaturementioning

confidence: 99%

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Divisive Algorithm Based on Node Clustering Coefficient for Community Detection

Jin

2020

IEEE Access

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This paper studies the relationship between the clustering coefficient of nodes and the community structure of the network. Communities in a network are regarded as node-induced subgraphs of the network in this study. We define the border of a subgraph and the node network density of a node and present a formal definition of community from the view of examining the subgraph borders. Afterward, we analyze the relationship between the change in node clustering coefficients and in node network density and set the rule for identifying intercommunity edges. Finally, we propose a novel divisive algorithm for community detection by iteratively removing intercommunity edges. The time complexity of our algorithm is O(N d 2), which increases linearly with the network size. Experiments on both synthetic and real-world networks show that introducing node clustering coefficients into the divisive algorithm can greatly improve the time efficiency of the algorithm while guaranteeing the accuracy of community detection.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: A Community Community Structure and Community Detectionmentioning

confidence: 99%

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Divisive Algorithm Based on Node Clustering Coefficient for Community Detection

Jin

2020

IEEE Access

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“…No entanto, a maioria das redes sociais evolui com o tempo, impulsionada pelas atividades e afiliações compartilhadas de seus membros [Kumar et al 2010]. Uma rede dinâmica é um tipo especial de redes complexas em evolução, nas quais as mudanças ocorrem ao longo do tempo [Xu et al 2020]. Barabási e colaboradores perceberam que as redes de colaboração científica poderiam ser um campo fértil para o estudo de redes dinâmicas [Barabási et al 2002].…”

Section: Introductionunclassified

BraSNAM em perspectiva: uma análise da sua trajetória até os 10 anos de existência

Lobato

Sousa²,

Jacob³

2021

Anais Do X Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2021)

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É inegável o aumento da pervasividade e relevância das mídias sociais em nosso cotidiano. Desde 2012, o Brazilian Workshop on Social Network Analysis and Mining (BraSNAM) representa um importante fórum para reunir pesquisadores a fim de discutir métodos de análise, tendências e fenômenos que ocorrem nas redes sociais. Neste 2021, este evento completa 10 anos, com 230 trabalhos apresentados até o momento. Além disso, possui uma comunidade composta por 527 pesquisadores e pesquisadoras de 95 diferentes instituições. À luz deste marco, esse trabalho apresentam uma análise da comunidade BraSNAM. Os resultados atestam o crescimento sustentável da comunidade, sobretudo em relação ao seu impacto técnico-científico. Os achados do trabalho podem auxiliar o comitê organizador no planejamento estratégico das próximas edições.

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“…Take social networks for instance, influential nodes are those that have the most spreading ability, or playing a predominant role in the network evolution. Notably, a popular star in online social media may remarkably accelerate the spreading of rumors, and a few super spreaders [ 2 ] could largely expand the epidemic prevalence of a disease (e.g., COVID-19) [ 3 ]. The research of influencer identification is beneficial to understanding and controlling the spreading dynamics in social networks with diverse applications such as epidemiology, collective dynamics and viral marketing [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying Influencers in Social Networks

Huang

Chen

Wang

et al. 2020

Entropy

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Social network analysis is a multidisciplinary research covering informatics, mathematics, sociology, management, psychology, etc. In the last decade, the development of online social media has provided individuals with a fascinating platform of sharing knowledge and interests. The emergence of various social networks has greatly enriched our daily life, and simultaneously, it brings a challenging task to identify influencers among multiple social networks. The key problem lies in the various interactions among individuals and huge data scale. Aiming at solving the problem, this paper employs a general multilayer network model to represent the multiple social networks, and then proposes the node influence indicator merely based on the local neighboring information. Extensive experiments on 21 real-world datasets are conducted to verify the performance of the proposed method, which shows superiority to the competitors. It is of remarkable significance in revealing the evolutions in social networks and we hope this work will shed light for more and more forthcoming researchers to further explore the uncharted part of this promising field.

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Superspreaders and superblockers based community evolution tracking in dynamic social networks

Cited by 22 publications

References 46 publications

Divisive Algorithm Based on Node Clustering Coefficient for Community Detection

Divisive Algorithm Based on Node Clustering Coefficient for Community Detection

BraSNAM em perspectiva: uma análise da sua trajetória até os 10 anos de existência

Identifying Influencers in Social Networks

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