Distributed graph summarization

Xingjie Liu, Yuanyuan Tian, Qi He, Wang Chien Lee, John McPherson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

Graph has been a ubiquitous and essential data representation to model real world objects and their relationships. Today, large amounts of graph data have been generated by various applications. Graph summarization techniques are crucial in uncovering useful insights about the patterns hidden in the underlying data. However, all existing works in graph summarization are single-process solutions, and as a result cannot scale to large graphs. In this paper, we introduce three distributed graph summarization algorithms to address this problem. Experimental results show that the proposed algorithms can produce good quality summaries and scale well with increasing data sizes. To the best of our knowledge, this is the first work to study distributed graph summarization methods.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages799-808
Number of pages10
ISBN (Electronic)9781450325981
DOIs
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Publication series

NameCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period11/3/1411/7/14

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

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