271 citations · 407 across the 5 of their papers we have counts for
5 papers
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Qitian Wu, Wentao Zhao, Zenan Li +2
Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing,…
Localized Contrastive Learning on Graphs
Hengrui Zhang, Qitian Wu, Yu Wang +3
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its qu…
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
Hengrui Zhang, Qitian Wu, Junchi Yan +2
We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data. It follows the previous methods that generate two views of an in…
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
Qitian Wu, Zixuan Zhang, Xiaofeng Gao +2
We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations. Such problem has been rarely studied by previous wor…
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Qitian Wu, Hengrui Zhang, Xiaofeng Gao +4
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. However, most existing models assu…