Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting Large-Scale Knowledge Bases from the Web
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Community detection in complex networks using collaborative evolutionary algorithms
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World Wide Web
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We propose an efficient and novel approach for discovering communities in real-world random networks. Communities are formed by subsets of nodes in a graph, which are closely related. Extraction of these communities facilitates better understanding of such networks. Community related research has focused on two main problems, community discovery and community identification. Community discovery is the problem of extracting all the communities in a given network whereas community identification is the problem of identifying the community to which a given set of nodes from the network belong. In this paper we first perform a brief survey of the existing community-discovery algorithms and then propose a novel approach to discovering communities using bibliographic metrics. We also test the proposed algorithm on real-world networks and on computer-generated models with known community structures.