15 citations · 40 across the 6 of their papers we have counts for
6 papers
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Jiahao Liu, Guixiang Ma, Fei Jiang +3
Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for cli…
Multi-View Multi-Graph Embedding for Brain Network Clustering Analysis
Ye Liu, Lifang He, Bokai Cao +3
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…
Multi-view Graph Embedding with Hub Detection for Brain Network Analysis
Guixiang Ma, Chun-Ta Lu, Lifang He +2
Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common n…
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Bokai Cao, Xiangnan Kong, Jingyuan Zhang +2
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc.…
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
Lifang He, Xiangnan Kong, Philip S. Yu +3
With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a top…
Discriminative Feature Selection for Uncertain Graph Classification
Xiangnan Kong, Philip S. Yu, Xue Wang +1
Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, et…