The community-search problem and how to plan a successful cocktail party

Mauro Sozio, Aristides Gionis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A lot of research in graph mining has been devoted in the discovery of communities. Most of the work has focused in the scenario where communities need to be discovered with only reference to the input graph. However, for many interesting applications one is interested in finding the community formed by a given set of nodes. In this paper we study a query-dependent variant of the community-detection problem, which we call the community-search problem: given a graph G, and a set of query nodes in the graph, we seek to find a subgraph of G that contains the query nodes and it is densely connected. We motivate a measure of density based on minimum degree and distance constraints, and we develop an optimum greedy algorithm for this measure. We proceed by characterizing a class of monotone constraints and we generalize our algorithm to compute optimum solutions satisfying any set of monotone constraints. Finally we modify the greedy algorithm and we present two heuristic algorithms that find communities of size no greater than a specified upper bound. Our experimental evaluation on real datasets demonstrates the efficiency of the proposed algorithms and the quality of the solutions we obtain.

Original languageEnglish
Title of host publicationKDD'10 - Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data
Pages939-948
Number of pages10
DOIs
Publication statusPublished - 7 Sept 2010
Externally publishedYes
Event16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010 - Washington, DC, United States
Duration: 25 Jul 201028 Jul 2010

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD-2010
Country/TerritoryUnited States
CityWashington, DC
Period25/07/1028/07/10

Keywords

  • Community detection
  • Graph algorithms
  • Graph mining
  • Social networks

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