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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Veröffentlicht in:	Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 S. 1201 - 1208
Hauptverfasser:	Moctar, Ahmed Ould Mohamed, Sarr, Idrissa
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 31.07.2017
Schriftenreihe:	ACM Conferences
Schlagworte:	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
ISBN:	1450349935, 9781450349932
Online-Zugang:	Volltext
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