Multi-view multi-graph embedding for brain network clustering analysis

Ye Liu, Lifang He*, Bokai Cao, Philip S. Yu, Ann B Ragin, Alex D. Leow

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by exploiting information from multiple neuroimaging modalities or views, we are able to obtain an embedding that is more useful than the embedding learned from an individual view. Therefore, multi-view multi-graph embedding becomes a crucial task. Currently only a few studies have been devoted to this topic, and most of them focus on vector-based strategy which will cause structural information contained in the original graphs lost. As a novel attempt to tackle this problem, we propose Multi-view Multi-graph Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi-graph brain networks. Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions.

Original languageEnglish (US)
Title of host publication32nd AAAI Conference on Artificial Intelligence, AAAI 2018
PublisherAAAI press
Pages117-124
Number of pages8
ISBN (Electronic)9781577358008
StatePublished - Jan 1 2018
Event32nd AAAI Conference on Artificial Intelligence, AAAI 2018 - New Orleans, United States
Duration: Feb 2 2018Feb 7 2018

Publication series

Name32nd AAAI Conference on Artificial Intelligence, AAAI 2018

Other

Other32nd AAAI Conference on Artificial Intelligence, AAAI 2018
CountryUnited States
CityNew Orleans
Period2/2/182/7/18

Fingerprint

Brain
Tensors
Neuroimaging
Electric network analysis
Experiments

Keywords

  • Brain network embedding
  • Multi-graph embedding
  • Multi-view learning
  • Tensor factorization

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Liu, Y., He, L., Cao, B., Yu, P. S., Ragin, A. B., & Leow, A. D. (2018). Multi-view multi-graph embedding for brain network clustering analysis. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (pp. 117-124). (32nd AAAI Conference on Artificial Intelligence, AAAI 2018). AAAI press.
Liu, Ye ; He, Lifang ; Cao, Bokai ; Yu, Philip S. ; Ragin, Ann B ; Leow, Alex D. / Multi-view multi-graph embedding for brain network clustering analysis. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. pp. 117-124 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).
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Liu, Y, He, L, Cao, B, Yu, PS, Ragin, AB & Leow, AD 2018, Multi-view multi-graph embedding for brain network clustering analysis. in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, AAAI press, pp. 117-124, 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, New Orleans, United States, 2/2/18.

Multi-view multi-graph embedding for brain network clustering analysis. / Liu, Ye; He, Lifang; Cao, Bokai; Yu, Philip S.; Ragin, Ann B; Leow, Alex D.

32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press, 2018. p. 117-124 (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).

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

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AB - Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by exploiting information from multiple neuroimaging modalities or views, we are able to obtain an embedding that is more useful than the embedding learned from an individual view. Therefore, multi-view multi-graph embedding becomes a crucial task. Currently only a few studies have been devoted to this topic, and most of them focus on vector-based strategy which will cause structural information contained in the original graphs lost. As a novel attempt to tackle this problem, we propose Multi-view Multi-graph Embedding (M2E) by stacking multi-graphs into multiple partially-symmetric tensors and using tensor techniques to simultaneously leverage the dependencies and correlations among multi-view and multi-graph brain networks. Extensive experiments on real HIV and bipolar disorder brain network datasets demonstrate the superior performance of M2E on clustering brain networks by leveraging the multi-view multi-graph interactions.

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Liu Y, He L, Cao B, Yu PS, Ragin AB, Leow AD. Multi-view multi-graph embedding for brain network clustering analysis. In 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. AAAI press. 2018. p. 117-124. (32nd AAAI Conference on Artificial Intelligence, AAAI 2018).