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ICDM
2010
IEEE
230views Data Mining» more  ICDM 2010»
13 years 5 months ago
Clustering Large Attributed Graphs: An Efficient Incremental Approach
In recent years, many networks have become available for analysis, including social networks, sensor networks, biological networks, etc. Graph clustering has shown its effectivenes...
Yang Zhou, Hong Cheng, Jeffrey Xu Yu
SDM
2010
SIAM
213views Data Mining» more  SDM 2010»
13 years 9 months ago
Spectral Analysis of Signed Graphs for Clustering, Prediction and Visualization
We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of gra...
Jérôme Kunegis, Stephan Schmidt, Andr...
CCR
2004
89views more  CCR 2004»
13 years 7 months ago
Should we build Gnutella on a structured overlay?
There has been much interest in both unstructured and structured overlays recently. Unstructured overlays, like Gnutella, build a random graph and use flooding or random walks on ...
Miguel Castro, Manuel Costa, Antony I. T. Rowstron
BMVC
2001
13 years 10 months ago
Graph Matching using Adjacency Matrix Markov Chains
This paper describes a spectral method for graph-matching. We adopt a graphical models viewpoint in which the graph adjacency matrix is taken to represent the transition probabili...
Antonio Robles-Kelly, Edwin R. Hancock
RSA
2011
157views more  RSA 2011»
13 years 2 months ago
The cover time of random geometric graphs
We study the cover time of random geometric graphs. Let I(d) = [0, 1]d denote the unit torus in d dimensions. Let D(x, r) denote the ball (disc) of radius r. Let Υd be the volume...
Colin Cooper, Alan M. Frieze