179 citations · 535 across the 16 of their papers we have counts for
6 papers · 1 filter
Continual Learning on Dynamic Graphs via Parameter Isolation
Peiyan Zhang, Yuchen Yan, Chaozhuo Li +4
Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetti…
Test-Time Training for Graph Neural Networks
Yiqi Wang, Chaozhuo Li, Wei Jin +4
Graph Neural Networks (GNNs) have made tremendous progress in the graph classification task. However, a performance gap between the training set and the test set has often been not…
Learning on Large-scale Text-attributed Graphs via Variational Inference
Jianan Zhao, Meng Qu, Chaozhuo Li +5
This paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both…
Domain-invariant Feature Exploration for Domain Generalization
Wang Lu, Jindong Wang, Haoliang Li +2
Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (…
Gophormer: Ego-Graph Transformer for Node Classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen +5
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area,…
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang +5
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified i…