34 citations · 54 across the 7 of their papers we have counts for
10 papers · 1 filter
Deep Manifold Learning with Graph Mining
Xuelong Li, Ziheng Jiao, Hongyuan Zhang +1
Admittedly, Graph Convolution Network (GCN) has achieved excellent results on graph datasets such as social networks, citation networks, etc. However, softmax used as the decision…
Matrix Completion via Non-Convex Relaxation and Adaptive Correlation Learning
Xuelong Li, Hongyuan Zhang, Rui Zhang
The existing matrix completion methods focus on optimizing the relaxation of rank function such as nuclear norm, Schatten-p norm, etc. They usually need many iterations to converge…
AnchorGAE: General Data Clustering via Bipartite Graph Convolution
Hongyuan Zhang, Jiankun Shi, Rui Zhang +1
Since the representative capacity of graph-based clustering methods is usually limited by the graph constructed on the original features, it is attractive to find whether graph neu…
Non-Gradient Manifold Neural Network
Rui Zhang, Ziheng Jiao, Hongyuan Zhang +1
Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent and thus has a slow convergence. In addition, softmax, as a decision layer, may i…
WGCN: Graph Convolutional Networks with Weighted Structural Features
Yunxiang Zhao, Jianzhong Qi, Qingwei Liu +1
Graph structural information such as topologies or connectivities provides valuable guidance for graph convolutional networks (GCNs) to learn nodes' representations. Existing GCN m…
LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
Jingwei Guo, Kaizhu Huang, Xinping Yi +1
Disentangled Graph Convolutional Network (DisenGCN) is an encouraging framework to disentangle the latent factors arising in a real-world graph. However, it relies on disentangling…