9 citations · 11 across the 18 of their papers we have counts for
5 papers · 1 filter
Structure-Aware Consensus Network on Graphs with Few Labeled Nodes
Shuaike Xu, Xiaolin Zhang, Peng Zhang +1
Graph node classification with few labeled nodes presents significant challenges due to limited supervision. Conventional methods often exploit the graph in a transductive learning…
Self-Contrastive Graph Diffusion Network
Yixian Ma, Kun Zhan
Augmentation techniques and sampling strategies are crucial in contrastive learning, but in most existing works, augmentation techniques require careful design, and their sampling…
Entropy Neural Estimation for Graph Contrastive Learning
Yixuan Ma, Xiaolin Zhang, Peng Zhang +1
Contrastive learning on graphs aims at extracting distinguishable high-level representations of nodes. In this paper, we theoretically illustrate that the entropy of a dataset can…
Stationary Diffusion State Neural Estimation for Multiview Clustering
Chenghua Liu, Zhuolin Liao, Yixuan Ma +1
Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, their performance limits to using a predefined graph. We argue tha…
Mutual Teaching for Graph Convolutional Networks
Kun Zhan, Chaoxi Niu
Graph convolutional networks produce good predictions of unlabeled samples due to its transductive label propagation. Since samples have different predicted confidences, we take hi…