Measuring and extracting proximity in networks

  • Authors:
  • Yehuda Koren;Stephen C. North;Chris Volinsky

  • Affiliations:
  • AT&T Labs - Research, Florham Park, NJ;AT&T Labs - Research, Florham Park, NJ;AT&T Labs - Research, Florham Park, NJ

  • Venue:
  • Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2006

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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.