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Communities from seed sets

Published: 23 May 2006 Publication History

Abstract

Expanding a seed set into a larger community is a common procedure in link-based analysis. We show how to adapt recent results from theoretical computer science to expand a seed set into a community with small conductance and a strong relationship to the seed, while examining only a small neighborhood of the entire graph. We extend existing results to give theoretical guarantees that apply to a variety of seed sets from specified communities. We also describe simple and flexible heuristics for applying these methods in practice, and present early experiments showing that these methods compare favorably with existing approaches.

References

[1]
Krishna Bharat and Monika R. Henzinger. Improved algorithms for topic distillation in a hyperlinked environment. In ACM SIGIR-98, pages 104--111, Melbourne, AU, 1998.
[2]
Soumen Chakrabarti, Byron E. Dom, and Piotr Indyk. Enhanced hypertext categorization using hyperlinks. In Laura M. Haas and Ashutosh Tiwary, editors, Proceedings of ACM SIGMOD-98, pages 307--318, Seattle, US, 1998. ACM Press, New York, US.
[3]
Fan Chung and Lincoln Lu. Connected components in random graphs with given degree sequences. Annals of Combinatorics, 6:125--145, 2002.
[4]
Gary Flake, Steve Lawrence, and C. Lee Giles. Efficient identification of web communities. In Sixth ACM SIGKDD, pages 150--160, Boston, MA, August 20--23 2000.
[5]
Zoltán Gyöngyi, Hector Garcia-Molina, and Jan Pedersen. Combating web spam with trustrank. In VLDB, pages 576--587, 2004.
[6]
George Karypis and Vipin Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20:359 -- 392, 1999.
[7]
Jon M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999.
[8]
Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, and Andrew Tomkins. Trawling the Web for emerging cyber-communities. Computer Networks, 31(11--16):1481--1493, 1999.
[9]
Kevin J Lang. Fixing two weaknesses of the spectral method. In NIPS, 2005.
[10]
László Lovász and Miklós Simonovits. The mixing rate of markov chains, an isoperimetric inequality, and computing the volume. In FOCS, pages 346--354, 1990.
[11]
László Lovász and Miklós Simonovits. Random walks in a convex body and an improved volume algorithm. Random Struct. Algorithms, 4(4):359--412, 1993.
[12]
Daniel A. Spielman and Shang-Hua Teng. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In ACM STOC-04, pages 81--90, New York, NY, USA, 2004. ACM Press.
[13]
M. Toyoda and M. Kitsuregawa. Creating a web community chart for navigating related communities, 2001.

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cover image ACM Conferences
WWW '06: Proceedings of the 15th international conference on World Wide Web
May 2006
1102 pages
ISBN:1595933239
DOI:10.1145/1135777
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 23 May 2006

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Author Tags

  1. community finding
  2. graph conductance
  3. link analysis
  4. random walks
  5. seed sets

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