Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Silk from a sow's ear: extracting usable structures from the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Trawling the Web for emerging cyber-communities
WWW '99 Proceedings of the eighth international conference on World Wide Web
Focused crawling: a new approach to topic-specific Web resource discovery
WWW '99 Proceedings of the eighth international conference on World Wide Web
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling (Multiagent Systems, Artificial Societies, and Simulated Organizations)
An Autonomy Oriented Computing (AOC) Approach to Distributed Network Community Mining
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
Community Mining from Signed Social Networks
IEEE Transactions on Knowledge and Data Engineering
Autonomy-oriented computing (AOC): formulating computational systems with autonomous components
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Local Search in Weighted and Directed Social Networks: The Case of Enron Email Networks
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
An Autonomy-Oriented Paradigm for Self-Organized Computing
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Fundamenta Informaticae - Methodologies for Intelligent Systems
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One of the central problems in studying and understanding complex networks, such as online social networks or World Wide Web, is to discover hidden, either physically (e.g., interactions or hyperlinks) or logically (e.g., profiles or semantics) well-defined topological structures. From a practical point of view, a good example of such structures would be so-called network communities. Earlier studies have introduced various formulations as well as methods for the problem of identifying or extracting communities. While each of them has pros and cons as far as the effectiveness and efficiency are concerned, almost none of them has explicitly dealt with the potential relationship between the global topological property of a network and the local property of individual nodes. In order to study this problem, this paper presents a new algorithm, called ICS, which aims to discover natural network communities by inferring from the local information of nodes inherently hidden in networks based on a new centrality, that is, clustering centrality, which is a generalization of eigenvector centrality. As compared with existing methods, our method runs efficiently with a good clustering performance. Additionally, it is insensitive to its built-in parameters and prior knowledge.