SMAC: Subgraph matching and centrality in huge social networks

Noseong Park, Michael Ovelgönne, V. S. Subrahmanian

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

1 Citation (Scopus)

Abstract

Classical centrality measures like betweenness, closeness, eigenvector, and degree centrality are application and user independent. They are also independent of graph semantics. However, in many applications, users have a clear idea of who they consider important in graphs where vertices and edges have properties, and the goal of this paper is to enable them to bring their knowledge to the table in defining centrality in graphs. We propose a novel combination of subgraph matching queries which have been studied extensively in the context of both RDF and social networks, and scoring functions. The resulting SMAC framework allows a user to define what he thinks are central vertices in a network via user-defined subgraph patterns and certain mathematical measures he specifies. We formally define SMAC queries and develop algorithms to compute answers to such queries. We test our algorithms on real-world data sets from CiteSeerX, Flickr, YouTube, and IMDb containing over 6M vertices and 15M edges and show that our algorithms work well in practice.

Original languageEnglish
Title of host publicationProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013
Pages134-141
Number of pages8
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013 - Washington, DC, United States
Duration: 2013 Sep 82013 Sep 14

Publication series

NameProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013

Conference

Conference2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013
CountryUnited States
CityWashington, DC
Period13/9/813/9/14

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Park, N., Ovelgönne, M., & Subrahmanian, V. S. (2013). SMAC: Subgraph matching and centrality in huge social networks. In Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013 (pp. 134-141). [6693324] (Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013). https://doi.org/10.1109/SocialCom.2013.27