18 Scopus citations

Abstract

Community detection is an unsupervised learning task that discovers groups such that group members share more similarities or interact more frequently among themselves than with people outside groups. In social media, link information can reveal heterogeneous relationships of various strengths, but often can be noisy. Since different sources of data in social media can provide complementary information, e.g., bookmarking and tagging data indicates user interests, frequency of commenting suggests the strength of ties, etc., we propose to integrate social media data of multiple types for improving the performance of community detection. We present a joint optimization framework to integrate multiple data sources for community detection. Empirical evaluation on both synthetic data and real-world social media data shows significant performance improvement of the proposed approach. This work elaborates the need for and challenges of multi-source integration of heterogeneous data types, and provides a principled way of multi-source community detection.

Original languageEnglish (US)
Title of host publicationModeling and Mining Ubiquitous Social Media - International Workshop, MSM 2011, MUSE 2011, Revised Selected Papers
Pages1-20
Number of pages20
DOIs
StatePublished - Oct 24 2012
Event2nd International Workshop on Modeling and Mining Ubiquitous Social Media, MSM 2011, MUSE 2011 - Boston, MA, United States
Duration: Oct 9 2011Oct 9 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7472 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Modeling and Mining Ubiquitous Social Media, MSM 2011, MUSE 2011
CountryUnited States
CityBoston, MA
Period10/9/1110/9/11

Keywords

  • Community Detection
  • Multi-source Integration
  • Social Media Data

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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