1 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
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…
On Lexical Invariance on Multisets and Graphs
Muhan Zhang
In this draft, we study a novel problem, called lexical invariance, using the medium of multisets and graphs. Traditionally in the NLP domain, lexical invariance indicates that the…
Foundations and Frontiers of Graph Learning Theory
Yu Huang, Min Zhou, Menglin Yang +7
Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network…
On the Stability of Expressive Positional Encodings for Graphs
Yinan Huang, William Lu, Joshua Robinson +4
Designing effective positional encodings for graphs is key to building powerful graph transformers and enhancing message-passing graph neural networks. Although widespread, using L…