Ego-centered community detection in directed and weighted networks
Community detection is one of the most studied topics in Social Network Analysis. Research in this realm has predominantly focus on finding out communities by considering the network as a whole. That is, all nodes are put in the same pool to define central metrics for finding out communities while i...
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| Vydáno v: | Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 s. 1201 - 1208 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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New York, NY, USA
ACM
31.07.2017
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| Edice: | ACM Conferences |
| Témata: |
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing design and evaluation methods
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing design and evaluation methods
> Social network analysis
Human-centered computing
> Collaborative and social computing
> Collaborative and social computing theory, concepts and paradigms
|
| ISBN: | 1450349935, 9781450349932 |
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| Abstract | Community detection is one of the most studied topics in Social Network Analysis. Research in this realm has predominantly focus on finding out communities by considering the network as a whole. That is, all nodes are put in the same pool to define central metrics for finding out communities while ignoring the particularity of some nodes and their impact. Yet, if the position of some nodes matters when defining the metrics (i.e. node centric approach), the found communities may differ and can make more sens in real life situations. For instance, identifying the communities based on drug dealers and their interactions with others sounds better than finding communities while ignoring the individuals status. The purpose of this paper is to detect ego-centered community, which is defined as a community built from a particular node. Our solution is set to combine both link direction and weight, and therefore, differs from many existing solutions. Basically, we rely on a metric called a quality function that uses link properties to assess the cohesion of identified groups. Our method detect communities that reflect not only the structure but the reality regarding to the interaction nature in terms of intensity. We implement our solution and use "Les Miserables" dataset to demonstrate the effectiveness of our solution. |
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| AbstractList | Community detection is one of the most studied topics in Social Network Analysis. Research in this realm has predominantly focus on finding out communities by considering the network as a whole. That is, all nodes are put in the same pool to define central metrics for finding out communities while ignoring the particularity of some nodes and their impact. Yet, if the position of some nodes matters when defining the metrics (i.e. node centric approach), the found communities may differ and can make more sens in real life situations. For instance, identifying the communities based on drug dealers and their interactions with others sounds better than finding communities while ignoring the individuals status. The purpose of this paper is to detect ego-centered community, which is defined as a community built from a particular node. Our solution is set to combine both link direction and weight, and therefore, differs from many existing solutions. Basically, we rely on a metric called a quality function that uses link properties to assess the cohesion of identified groups. Our method detect communities that reflect not only the structure but the reality regarding to the interaction nature in terms of intensity. We implement our solution and use "Les Miserables" dataset to demonstrate the effectiveness of our solution. |
| Author | Moctar, Ahmed Ould Mohamed Sarr, Idrissa |
| Author_xml | – sequence: 1 givenname: Ahmed Ould Mohamed surname: Moctar fullname: Moctar, Ahmed Ould Mohamed email: amed.mohameden@gmail.com organization: Université Cheikh Anta Diop de Dakar, Dpt. Mathématique-Informatique, Dakar - Fann, Senegal – sequence: 2 givenname: Idrissa surname: Sarr fullname: Sarr, Idrissa email: idrissa.sarr@ucad.edu.sn organization: Université Cheikh Anta Diop de Dakar, Dpt. Mathématique-Informatique, Dakar - Fann, Senegal |
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| Copyright | 2017 ACM |
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| DOI | 10.1145/3110025.3121243 |
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| Editor | Diesner, Jana Ferrari, Elena Xu, Guandong |
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| PublicationTitle | Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 ASONAM 2017 : proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining : Sydney, Australia, 31 July-03 August, 2017 |
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| SubjectTerms | Applied computing Applied computing -- Law, social and behavioral sciences Applied computing -- Law, social and behavioral sciences -- Sociology Human-centered computing Human-centered computing -- Collaborative and social computing Human-centered computing -- Collaborative and social computing -- Collaborative and social computing design and evaluation methods Human-centered computing -- Collaborative and social computing -- Collaborative and social computing design and evaluation methods -- Social network analysis Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms Human-centered computing -- Collaborative and social computing -- Collaborative and social computing theory, concepts and paradigms -- Social networks Information systems Information systems -- Information systems applications Information systems -- Information systems applications -- Data mining Information systems -- World Wide Web Information systems -- World Wide Web -- Web applications Information systems -- World Wide Web -- Web applications -- Social networks Networks Networks -- Network types Networks -- Network types -- Overlay and other logical network structures Networks -- Network types -- Overlay and other logical network structures -- Online social networks |
| Title | Ego-centered community detection in directed and weighted networks |
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