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» Context-Aware Object Connection Discovery in Large Graphs
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WWW
2010
ACM
13 years 11 months ago
Empirical comparison of algorithms for network community detection
Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses...
Jure Leskovec, Kevin J. Lang, Michael W. Mahoney
COMSWARE
2008
IEEE
13 years 6 months ago
Extracting dense communities from telecom call graphs
Social networks refer to structures made of nodes that represent people or other entities embedded in a social context, and whose edges represent interaction between entities. Typi...
Vinayaka Pandit, Natwar Modani, Sougata Mukherjea,...
KDD
2004
ACM
190views Data Mining» more  KDD 2004»
14 years 5 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
HT
1991
ACM
13 years 8 months ago
Identifying Aggregates in Hypertext Structures
Hypertext systems are being used in many applications because of their flexible structure and the great browsing freedom they give to diverse communities of users. However, this s...
Rodrigo A. Botafogo, Ben Shneiderman
NIPS
2001
13 years 6 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
Mikhail Belkin, Partha Niyogi