Egocentric information abstraction for heterogeneous social networks

Cheng-Te Li, Shou De Lin

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

15 Citations (Scopus)

Abstract

Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify linear combination of relations as features and compute statistical dependencies as feature values. Then we design several abstraction criteria to distill representative and important information to construct the abstracted graphs for visualization. The evaluations conducted on a real world movie dataset and an artificial crime dataset demonstrate that the abstractions can indeed retain important information and facilitate more accurate and efficient human analysis.

Original languageEnglish
Title of host publicationProceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
Pages255-260
Number of pages6
DOIs
Publication statusPublished - 2009 Oct 15
Event2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009 - Athens, Greece
Duration: 2009 Jul 202009 Jul 22

Publication series

NameProceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009

Other

Other2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM 2009
CountryGreece
CityAthens
Period09-07-2009-07-22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Social Sciences(all)

Fingerprint Dive into the research topics of 'Egocentric information abstraction for heterogeneous social networks'. Together they form a unique fingerprint.

Cite this