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Mining scale-free networks using geodesic clustering

2004
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Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '04
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Many real-world graphs have been shown to be scale-freevertex degrees follow power law distributions, vertices tend to cluster, and the average length of all shortest paths is small. We present a new model for understanding scale-free networks based on multilevel geodesic approximation, using a new data structure called a multilevel mesh. Using this multilevel framework, we propose a new kind of graph clustering for data reduction of very large graph systems such as social, biological, or

doi:10.1145/1014052.1014146
dblp:conf/kdd/WuGH04
fatcat:63l33dxctjhhxdgi2jnco47d6y