2 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2023
Do Not Train It: A Linear Neural Architecture Search of Graph Neural Networks
Peng Xu, Lin Zhang, Xuanzhou Liu +4
Neural architecture search (NAS) for Graph neural networks (GNNs), called NAS-GNNs, has achieved significant performance over manually designed GNN architectures. However, these me…
cs.LG2023★ 1 cited
D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching
Xuanzhou Liu, Lin Zhang, Jiaqi Sun +2
Subgraph matching is a fundamental building block for graph-based applications and is challenging due to its high-order combinatorial nature. Existing studies usually tackle it by…
cs.LG2023★ 2 cited
Feature Expansion for Graph Neural Networks
Jiaqi Sun, Lin Zhang, Guangyi Chen +3
Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification. Recently, many methods have studi…