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
A Distributed Algorithm for finding All Maximal Cliques in a Network Graph
LATIN '92 Proceedings of the 1st Latin American Symposium on Theoretical Informatics
Graph Drawing Software
Email as spectroscopy: automated discovery of community structure within organizations
Communities and technologies
Community Mining from Signed Social Networks
IEEE Transactions on Knowledge and Data Engineering
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Distributed spatial clustering in sensor networks
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Autonomy-oriented computing (AOC): formulating computational systems with autonomous components
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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
Agent and multi-agent applications to support distributed communities of practice: a short review
Autonomous Agents and Multi-Agent Systems
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A network community refers to a special type of network structure that contains a group of nodes connected based on certain relationships or similar properties. Our ability to mine communities hidden inside networks will readily enable us to effectively understand and exploit such networks. So far, various methods and algorithms have been developed to perform the task of community mining, where it is often required that the networks are processed in a centralized manner, and their structures will not dynamically change. However, in the real world, many applications involve distributed and dynamically evolving networks, in which resources and controls are not only decentralized but also updated frequently. It would be difficult for the existing methods to deal with these types of networks since their global topological representations are either not available or too hard to obtain due to their huge size, decentralization, and/or dynamic updates. The aim of our work is to address the problem of mining communities from a distributed and dynamic network. It differs from the previous ones in that here we introduce the notion of self-organizing agent networks, and provide an autonomy-oriented computing (AOC) approach to distributed and incremental mining of network communities. The AOC-based method utilizes reactive agents that can collectively detect and update community structures in a distributed and dynamically evolving network, based only on their local views and interactions. While providing detailed formulations, we present the results of our systematic validations using real-world benchmark networks as well as synthetic networks that include a distributed intelligent Portable Digital Assistant (iPDA) network example.