Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis


  • Ye Liu University of Illinois at Chicago
  • Lifang He Cornell University
  • Bokai Cao University of Illinois at Chicago
  • Philip Yu University of Illinois at Chicago; Tsinghua University
  • Ann Ragin Northwestern University
  • Alex Leow University of Illinois at Chicago


Brain Network Embedding, Multi-view Learning, Tensor Factorization


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.




How to Cite

Liu, Y., He, L., Cao, B., Yu, P., Ragin, A., & Leow, A. (2018). Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from