3 papers
cs.LG2026
Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors
Junru Zhou, Cai Zhou, Xiyuan Wang +2
A popular way to improve the expressive power of graph neural networks (GNNs) is to use Laplacian eigenvectors as additional node features, since they can serve both as structural…
cs.LG2025
Branching Strategies Based on Subgraph GNNs: A Study on Theoretical Promise versus Practical Reality
Junru Zhou, Yicheng Wang, Pan Li
Graph Neural Networks (GNNs) have emerged as a promising approach for ``learning to branch'' in Mixed-Integer Linear Programming (MILP). While standard Message-Passing GNNs (MPNNs)…
cs.LG2024
Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective
Junru Zhou, Muhan Zhang
The ability of graph neural networks (GNNs) to count homomorphisms has recently been proposed as a practical and fine-grained measure of their expressive power. Although several ex…