Community search over big graphs: Models, algorithms, and opportunities

Xin HUANG, Laks V.S. Lakshmanan, Jianliang XU

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

32 Citations (Scopus)

Abstract

Communities serve as basic structures for understanding the organization of many real-world networks, such as social, biological, collaboration, and communication networks. Recently, community search over large graphs has attracted significantly increasing attention, from simple and static graphs to evolving, attributed, location-based graphs. Different from the well-studied problem of community detection that finds all communities in an entire network, community search is to find the cohesive communities w.r.t. the query nodes. In this tutorial, we survey the state-of-The-Art of community search on various kinds of networks across different application areas such as densely-connected community search, attributed community search, social circle discovery, and querying geosocial groups. We first highlight the challenges posed by the community search problems. We continue the presentation of their principles, methodologies, algorithms, and applications, and give a comprehensive comparison of the state-of-The-Art techniques. This tutorial finally concludes by offering future directions for research in this important and growing area.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages1451-1454
Number of pages4
ISBN (Electronic)9781509065431
DOIs
Publication statusPublished - 16 May 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: 19 Apr 201722 Apr 2017

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Conference

Conference33rd IEEE International Conference on Data Engineering, ICDE 2017
Country/TerritoryUnited States
CitySan Diego
Period19/04/1722/04/17

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

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