94 citations · 261 across the 13 of their papers we have counts for
5 papers · 1 filter
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang +5
Graph Neural Networks (GNNs) have achieved great success in modeling graph-structured data. However, recent works show that GNNs are vulnerable to adversarial attacks which can foo…
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Yaochen Xie, Zhao Xu, Jingtun Zhang +2
Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new…
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
Lei Cai, Zhengyang Wang, Rob Kulathinal +2
Predictive modeling is useful but very challenging in biological image analysis due to the high cost of obtaining and labeling training data. For example, in the study of gene inte…
Second-Order Pooling for Graph Neural Networks
Zhengyang Wang, Shuiwang Ji
Graph neural networks have achieved great success in learning node representations for graph tasks such as node classification and link prediction. Graph representation learning re…
Non-Local Graph Neural Networks
Meng Liu, Zhengyang Wang, Shuiwang Ji
Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on di…