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PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
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Natural communities in large linked networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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Incremental personalized web page mining utilizing self-organizing HCMAC neural network
Web Intelligence and Agent Systems
Modular organization of protein interaction networks
Bioinformatics
Web Intelligence and Agent Systems
Two-phase Web site classification based on Hidden Markov Tree models
Web Intelligence and Agent Systems
Temporal multi-page summarization
Web Intelligence and Agent Systems
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
An analysis of social network-based Sybil defenses
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MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Influence maximizing and local influenced community detection based on multiple spread model
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Relative Validity Criteria for Community Mining Algorithms
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Social moms and health: a multi-platform analysis of mommy communities
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
CooL-AgentSpeak: Endowing AgentSpeak-DL agents with plan exchange and ontology services
Web Intelligence and Agent Systems
RoClust: Role discovery for graph clustering
Web Intelligence and Agent Systems
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In this paper, three new algorithms, a greedy algorithm, a KL-like algorithm, and an add-all algorithm, are proposed to find local optimal community structures in large networks starting from a given source vertex. The time complexity for finding a local community of all these algorithms is O(K 2d), where K is the number of vertices to be explored in the sub-graph and d is the average degree of the vertices in the sub-graph. A JAVA tool is developed based on these algorithms to identify local community structures in large networks. The results of using this tool to analyze a co-purchase network from Amazon.com show that local community structures exist in this large-scale co-purchase network. Further analyses of the identified local communities show that purchases of media items form more compact local communities than purchases of book items do, indicating that recommending digital media items to customers based on co-purchasing information in the online store is more efficient than recommending books.