7 citations · 11 across the 20 of their papers we have counts for
8 papers · 1 filter
Exact Acceleration of Subgraph Graph Neural Networks by Eliminating Computation Redundancy
Qian Tao, Xiyuan Wang, Muhan Zhang +3
Graph neural networks (GNNs) have become a prevalent framework for graph tasks. Many recent studies have proposed the use of graph convolution methods over the numerous subgraphs o…
GL-Fusion: Rethinking the Combination of Graph Neural Network and Large Language model
Haotong Yang, Xiyuan Wang, Qian Tao +3
Recent research on integrating Large Language Models (LLMs) with Graph Neural Networks (GNNs) typically follows two approaches: LLM-centered models, which convert graph data into t…
Reconsidering the Performance of GAE in Link Prediction
Weishuo Ma, Yanbo Wang, Xiyuan Wang +1
Recent advancements in graph neural networks (GNNs) for link prediction have introduced sophisticated training techniques and model architectures. However, reliance on outdated bas…
Geometric Representation Condition Improves Equivariant Molecule Generation
Zian Li, Cai Zhou, Xiyuan Wang +2
Recent advances in molecular generative models have demonstrated great promise for accelerating scientific discovery, particularly in drug design. However, these models often strug…
Efficient Neural Common Neighbor for Temporal Graph Link Prediction
Xiaohui Zhang, Yanbo Wang, Xiyuan Wang +1
Temporal graphs are widespread in real-world applications such as social networks, as well as trade and transportation networks. Predicting dynamic links within these evolving grap…
Graph as Point Set
Xiyuan Wang, Pan Li, Muhan Zhang
Graph is a fundamental data structure to model interconnections between entities. Set, on the contrary, stores independent elements. To learn graph representations, current Graph N…
On the Completeness of Invariant Geometric Deep Learning Models
Zian Li, Xiyuan Wang, Shijia Kang +1
Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features…
Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
Cai Zhou, Xiyuan Wang, Muhan Zhang
In this paper, we propose the first framework that enables solving graph learning tasks of all levels (node, edge and graph) and all types (generation, regression and classificatio…