Detecting Overlapping Communities of Nodes with Multiple Attributes from Heterogeneous Networks

Kamal Taha, Paul D. Yoo

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

4 Scopus citations

Abstract

Many methods have been proposed for detecting communities from heterogeneous information networks with general topologies. However, most of these methods can detect communities with homogeneous structures containing nodes with only a single attribute. Investigating methods for detecting communities containing nodes with multiple attributes from heterogeneous information networks with general topologies has been understudied. Such communities are realistic in real-world social structures and exhibits many interesting properties. Towards this, we propose a system called DOMAIN that can detect overlapping communities of nodes with multiple attributes from heterogeneous information networks with general topologies. The framework of DOMAIN focuses on domains (i.e., attributes) that describe human characteristics such as ethnicity, culture, religion, demographic, age, or the like. The ultimate objective of the framework is to detect the smallest sub-communities with the largest possible number of domains, to which an active user belongs. The smaller a sub-community is, the more specific and granular its interests are. The interests of such a sub-community is the union of the interests and characteristics of the single domain communities, from which it is constructed. We evaluated DOMAIN by comparing it experimentally with three methods. Results revealed marked improvement.

Original languageBritish English
Title of host publicationCollaborative Computing
Subtitle of host publicationNetworking, Applications and Worksharing - 15th EAI International Conference, CollaborateCom 2019, Proceedings
EditorsXinheng Wang, Honghao Gao, Muddesar Iqbal, Geyong Min
PublisherSpringer
Pages760-779
Number of pages20
ISBN (Print)9783030301453
DOIs
StatePublished - 2019
Event15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019 - London, United Kingdom
Duration: 19 Aug 201922 Aug 2019

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume292
ISSN (Print)1867-8211

Conference

Conference15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019
Country/TerritoryUnited Kingdom
CityLondon
Period19/08/1922/08/19

Keywords

  • Community detection
  • Heterogeneous information networks
  • Multi-domain community
  • Overlapping communities
  • Social networks

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