The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
ACM Transactions on Internet Technology (TOIT)
Node roles and community structure in networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Community detection in complex networks
Journal of Computer Science and Technology
Ontology paper: Community analysis through semantic rules and role composition derivation
Web Semantics: Science, Services and Agents on the World Wide Web
Supporting information spread in a social internetworking scenario
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Recent graph-theoretic approaches have demonstrated remarkable success for ranking networked entities, including degree, closeness, betweenness, etc. They are mainly considering the local link factors only, while not so much work concentrates on the social influence ranking based on the local structure in social network. In this paper, two new social influence ranking metrics, InnerPagerank and OutterPagerank are proposed based on the concept of modified Pagerank, by considering the community structure knowledge. It is well adapted to direct and weighted networks also. Using the two metrics, we also show how to assign community-based node roles to the nodes, which is an effective supplement for single metric used as social influence measure. Identifying and understanding the node's social influence and role is of tremendous interest from both analysis and application points of view. This method is shown to give rasonable results than previous metrics both on test networks and real networks.