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Efficient identification of Web communities
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000We de ne a communit y on the web as a set of sites that have more links (in either direction) to members of the community than to non-members. Members of such a community can be eAEciently iden ti ed in a maximum ow / minim um cut framework, where the source is composed of known members, and the sink consists of well-kno wn non-members.
Gary William Flake +2 more
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Identification of Community Structure in Networks
2009 International Joint Conference on Computational Sciences and Optimization, 2009In recent years, researchers show considerable interests in identification of communities in complex networks. Diverse methods have been developed to address this problem. This paper presents a new algorithm for identifying communities in complex networks. The algorithm was inspired by studies on the effect of structural holes in networks.
Jun Chen, Tieju Ma, Chunjie Chi
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BROADER PERCEPTION FOR LOCAL COMMUNITY IDENTIFICATION
Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, 2010KDIR 2010, 25 oktober ...
Koopmans, F.T.W., Weide, T.P. van der
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An effective solution for community identification
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, 2009A popular method now widely used relies on the optimization of a quantity called modularity, which is a quality index for a partition of a network into communities. Current approaches, however, have difficulty splitting two clusters that are densely connected by one or more hub vertices.
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Leaker Identification in Multicast Communication
2012Multicast is a communication mode in which data is exchanged among multiple end systems. An important concern in multicast communications is the protection of copyrights. While preventing copyright violations seems very difficult, tracing copyright violators (leaker identification) is more feasible and can be used as a deterrence alternative.
Emad Eldin Mohamed, Driss Guerchi
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Only connect? Communication, identification, and autism
Social Neuroscience, 2007In this paper, we elaborate a theoretical position and report an empirical study on a specific form of interpersonal engagement: the propensity to identify with the subjective orientation of another person. On the basis of a hypothesis that individuals with autism have a relative lack of this form of intersubjective connectedness (Hobson, 1993, 2002 ...
R Peter, Hobson +2 more
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Community Identification in Directed Networks
2009The most common approach to community identification of directed networks has been to ignore edge directions and apply methods developed for undirected networks. Recently, Leicht and Newman published a work on community identification of directed networks, which is a generalization of the widely used community finding technique of modularity ...
Youngdo Kim +2 more
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Community Structure Identification: A Probabilistic Approach
2009 International Conference on Machine Learning and Applications, 2009A large variety of techniques has been developed for community structure identification (CSI) including modularity optimization, graph partitioning, and hierarchical clustering. In this paper, we argue that generative models are a promising approach for community structure identification, although these models have received very little attention from ...
Nacim Fateh Chikhi +2 more
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In situ identification of microorganisms in biofilm communities
Journal of Bioscience and Bioengineering, 2002Diverse microorganisms form complex microbial communities and usually exist in biofilm communities in both natural environments and engineered systems such as a wastewater treatment process. However, the conventional approach to investigate microbial ecology has not contributed to the understanding and clarification of the structure and function of ...
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Efficient community identification in complex networks
Social Network Analysis and Mining, 2012Complex networks are large, dynamic, random graphs modeled to replicate interactions among entities in real-world complex systems (e.g., the Internet, the World Wide Web, online social networks—Facebook, Twitter, etc., and the human connectome). These networks differ from the classical Erdos–Renyi random graphs in terms of network properties such as ...
Mahadevan Vasudevan, Narsingh Deo
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