A Parallel Network Community Detection Algorithm Based on Distance Dynamics

In recent years, community detection has drawn more and more researchers' attention. With the development of Internet, the scale of network data is growing fast. It is necessary to find an effective parallel community detection algorithm for large-scale network. In this paper, we propose a nove...

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Veröffentlicht in:	2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) S. 819 - 826
Hauptverfasser:	Wu, Bin, Zhang, Cuiyun, Guo, Qian
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 31.07.2017
Schriftenreihe:	ACM Conferences
Schlagworte:	community detection Computing methodologies Computing methodologies > Machine learning Computing methodologies > Machine learning > Learning paradigms Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning distance dynamics Human-centered computing Human-centered computing > Collaborative and social computing 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 > Data management systems Information systems > Data management systems > Database design and models Information systems > Data management systems > Database design and models > Graph-based database models 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 NMI parallelization Theory of computation Theory of computation > Design and analysis of algorithms Theory of computation > Design and analysis of algorithms > Distributed algorithms Theory of computation > Design and analysis of algorithms > Distributed algorithms > MapReduce algorithms Theory of computation > Design and analysis of algorithms > Graph algorithms analysis Theory of computation > Design and analysis of algorithms > Graph algorithms analysis > Dynamic graph algorithms Theory of computation > Theory and algorithms for application domains Theory of computation > Theory and algorithms for application domains > Machine learning theory Theory of computation > Theory and algorithms for application domains > Machine learning theory > Unsupervised learning and clustering NMI parallelization community detection distance dynamics
ISBN:	1450349935, 9781450349932
ISSN:	2473-991X
Online-Zugang:	Volltext
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