3 citations · 7 across the 4 of their papers we have counts for
4 papers
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
Yuankai Luo, Lei Shi, Xiao-Ming Wu
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-…
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
Yuankai Luo, Lei Shi, Xiao-Ming Wu
Graph Transformers (GTs) have recently emerged as popular alternatives to traditional message-passing Graph Neural Networks (GNNs), due to their theoretically superior expressivene…
Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning
Yuankai Luo, Hongkang Li, Qijiong Liu +2
We present a novel end-to-end framework that generates highly compact (typically 6-15 dimensions), discrete (int4 type), and interpretable node representations, termed node identif…
Improving Self-supervised Molecular Representation Learning using Persistent Homology
Yuankai Luo, Lei Shi, Veronika Thost
Self-supervised learning (SSL) has great potential for molecular representation learning given the complexity of molecular graphs, the large amounts of unlabelled data available, t…