Mining application-aware community organization with expanded feature subspaces from concerned attributes in social networks

Social networks are typical attributed networks with node attributes. Traditional attribute community detection problems aim at obtaining the whole set of communities in the network. Different from them, we study an application-oriented problem of mining an application-aware community organization w...

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Veröffentlicht in:Knowledge-based systems Jg. 139; S. 1 - 12
Hauptverfasser: Wu, Peng, Pan, Li
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.01.2018
Elsevier Science Ltd
Schlagworte:
Algorithms
Application
Attributes
Clustering
Communities
Community detection
Community organizations
Community structure
Community work
Concerns
Data mining
Efficiency
Experiments
Fitness
Mining
Mining industry
Optimization
Organizational effectiveness
Semi-supervised clustering
Social networks
Subspaces
Community detection
Social networks
91D30
Semi-supervised clustering
68P10
ISSN:0950-7051, 1872-7409
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Zusammenfassung:Social networks are typical attributed networks with node attributes. Traditional attribute community detection problems aim at obtaining the whole set of communities in the network. Different from them, we study an application-oriented problem of mining an application-aware community organization with respect to a set of specific concerned attributes. The set of concerned attributes is provided based on the requirements of any application by a user in advance. The application-aware community organization w.r.t. the set of concerned attributes consists of the communities whose attribute subspaces contain such set of concerned attributes. Besides concerned attributes, the subspace of each required community may contain some other relevant attributes. All relevant attributes of a subspace jointly describe and determine the community embedded in such subspace. Thus the problem includes two subproblems, i.e., how to expand the set of concerned attributes to complete subspaces and how to mine the communities embedded in the expanded subspaces. Two subproblems are jointly solved by optimizing a quality function called subspace fitness. An algorithm called ACM is proposed. In order to locate the communities potentially belonging to the application-aware community organization, a network backbone composed of nodes with similar concerned attributes is constructed. Then the cohesive parts of the network backbone are detected and set as the community seeds to locate the required communities. The set of concerned attributes is set as the initial subspace for all communities. Then each community and its attribute subspace are adjusted iteratively to optimize the subspace fitness. Extensive experiments on synthetic datasets demonstrate the effectiveness and efficiency of our method and applications on real-world networks show its application values.
Bibliographie:ObjectType-Article-1
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2017.10.007