93 citations · 139 across the 6 of their papers we have counts for
6 papers
Hierarchical Transformer for Scalable Graph Learning
Wenhao Zhu, Tianyu Wen, Guojie Song +2
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning…
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Junfei Wu, Weizhi Xu, Qiang Liu +2
The prevalence and perniciousness of fake news have been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper,…
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily
Xiaojun Ma, Qin Chen, Yuanyi Ren +2
Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream…
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
Wen Zhang, Shumin Deng, Mingyang Chen +5
Knowledge Graphs (KGs), representing facts as triples, have been widely adopted in many applications. Reasoning tasks such as link prediction and rule induction are important for t…
Streaming Graph Neural Networks via Continual Learning
Junshan Wang, Guojie Song, Yi Wu +1
Graph neural networks (GNNs) have achieved strong performance in various applications. In the real world, network data is usually formed in a streaming fashion. The distributions o…
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
Wen Zhang, Bibek Paudel, Liang Wang +5
Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings c…