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Measuring and extracting proximity in networks

Published:20 August 2006Publication History

ABSTRACT

Measuring distance or some other form of proximity between objects is a standard data mining tool. Connection subgraphs were recently proposed as a way to demonstrate proximity between nodes in networks. We propose a new way of measuring and extracting proximity in networks called "cycle free effective conductance"(CFEC). Our proximity measure can handle more than two endpoints, directed edges, is statistically well-behaved, and produces an effectiveness score for the computed subgraphs. We provide an efficien talgorithm. Also, we report experimental results and show examples for three large network data sets: a telecommunications calling graph, the IMDB actors graph, and an academic co-authorship network.

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        cover image ACM Conferences
        KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
        August 2006
        986 pages
        ISBN:1595933395
        DOI:10.1145/1150402

        Copyright © 2006 ACM

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        • Published: 20 August 2006

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