Discovering Fuzzy Structural Patterns for Graph Analytics

Many real-world datasets can be represented as attributed graphs that contain vertices, each of which is associated with a set of attribute values. Discovering clusters, or communities, which are structural patterns in these graphs, are one of the most important tasks in graph analysis. To perform t...

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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 26; H. 5; S. 2785 - 2796
Hauptverfasser:	He, Tiantian, Chan, Keith C. C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Analytics Attributed graph biological network Clustering algorithms Clusters community detection complex network Datasets fuzzy clustering fuzzy graph clustering fuzzy structural pattern graph analytics Graphical representations Graphs Image edge detection Optimization relational fuzzy c-means (FCM) clustering social network Social network services Topology
ISSN:	1063-6706, 1941-0034
Online-Zugang:	Volltext
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