Recursive structure similarity: A novel algorithm for graph clustering

Han Huhh, Yixin Fang, Rouming Jin, Wei Xiong, Xiaoning Qian, Dejing Dou, Hai Phan

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

Abstract

A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE 30th International Conference on Tools with Artificial Intelligence, ICTAI 2018
PublisherIEEE Computer Society
Pages395-400
Number of pages6
ISBN (Electronic)9781538674499
DOIs
StatePublished - Dec 13 2018
Event30th International Conference on Tools with Artificial Intelligence, ICTAI 2018 - Volos, Greece
Duration: Nov 5 2018Nov 7 2018

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2018-November
ISSN (Print)1082-3409

Other

Other30th International Conference on Tools with Artificial Intelligence, ICTAI 2018
CountryGreece
CityVolos
Period11/5/1811/7/18

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Science Applications

Keywords

  • Graph clustering
  • Social network

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