Multi-view graph embedding with hub detection for brain network analysis

Guixiang Ma, Chun Ta Lu, Lifang He, Philip S. Yu, Ann B. Ragin

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

12 Scopus citations

Abstract

Multi-view graph embedding and hub detection have both become widely studied problems in the area of graph learning. Both graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works considered joint learning the multi-view embeddings and the hubs from multi-view graph data. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit from each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
EditorsGeorge Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages967-972
Number of pages6
ISBN (Electronic)9781538638347
DOIs
StatePublished - Dec 15 2017
Event17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States
Duration: Nov 18 2017Nov 21 2017

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2017-November
ISSN (Print)1550-4786

Other

Other17th IEEE International Conference on Data Mining, ICDM 2017
CountryUnited States
CityNew Orleans
Period11/18/1711/21/17

ASJC Scopus subject areas

  • Engineering(all)

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