Multi-graph clustering based on interior-node topology with applications to brain networks

Guixiang Ma, Lifang He*, Bokai Cao, Jiawei Zhang, Philip S. Yu, Ann B. Ragin

*Corresponding author for this work

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

8 Scopus citations

Abstract

Learning from graph data has been attracting much attention recently due to its importance in many scientific applications, where objects are represented as graphs. In this paper, we study the problem of multi-graph clustering (i.e., clustering multiple graphs). We propose a multi-graph clustering approach (MGCT) based on the interior-node topology of graphs. Specifically, we extract the interior-node topological structure of each graph through a sparsity-inducing interior-node clustering. We merge the interior-node clustering stage and the multi-graph clustering stage into a unified iterative framework, where the multi-graph clustering will influence the interior-node clustering and the updated interior-node clustering results will be further exerted on multi-graph clustering. We apply MGCT on two real brain network data sets (i.e., ADHD and HIV). Experimental results demonstrate the superior performance of the proposed model on multi-graph clustering.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
EditorsJilles Giuseppe, Niels Landwehr, Giuseppe Manco, Paolo Frasconi
PublisherSpringer Verlag
Pages476-492
Number of pages17
ISBN (Print)9783319461274
DOIs
StatePublished - 2016
Event15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy
Duration: Sep 19 2016Sep 23 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9851 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
Country/TerritoryItaly
CityRiva del Garda
Period9/19/169/23/16

Keywords

  • Brain network
  • Interior-node topology
  • Multi-graph clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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