Summarizing big graphs by means of pseudo-boolean constraints

Said Jabbour, Nizar Mhadhbi, Abdesattar Mhadhbi, Badran Radaoui, Lakhdar Sais

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

Abstract

How to succinctly represent the truly relevant information in big data graphs? The approach presented in this paper aims to discover hidden graph structures and exploit them to compactly summarize large graphs. First, we show that some special graph classes such as cliques and bicliques can be represented efficiently as Pseudo-Boolean (PB) constraints. Then, we propose three new graph classes representable as PB constraints, called nested, sequence and clique-nested bi-partite graphs. Finally, we derive a general approach for partial or complete summarization of an arbitrary graph as a disjunction of PB constraints. Our representation can be seen as an original way to represent the edges of the graph, as they correspond to particular solutions of the PB constraints. An extensive experimental evaluation on several real-world networks shows that our framework is competitive with the state-of-the-art compression technique.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsJames Joshi, George Karypis, Ling Liu, Xiaohua Tony Hu, Ronay Ak, Yinglong Xia, Weijia Xu, Aki-Hiro Sato, Sudarsan Rachuri, Lyle Ungar, Philip S. Yu, Rama Govindaraju, Toyotaro Suzumura
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages889-894
Number of pages6
ISBN (Electronic)9781467390040
DOIs
Publication statusPublished - 1 Jan 2016
Externally publishedYes
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: 5 Dec 20168 Dec 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Conference

Conference4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States
CityWashington
Period5/12/168/12/16

Keywords

  • Graph Mining
  • Graph Summarization
  • Pseudo Boolean Constraints

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