95 citations · 217 across the 25 of their papers we have counts for
5 papers · 1 filter
Weisfeiler-Lehman meets Gromov-Wasserstein
Samantha Chen, Sunhyuk Lim, Facundo Mémoli +2
The Weisfeiler-Lehman (WL) test is a classical procedure for graph isomorphism testing. The WL test has also been widely used both for designing graph kernels and for analyzing gra…
Graph Coarsening with Neural Networks
Chen Cai, Dingkang Wang, Yusu Wang
As large-scale graphs become increasingly more prevalent, it poses significant computational challenges to process, extract and analyze large graph data. Graph coarsening is one po…
A Note on Over-Smoothing for Graph Neural Networks
Chen Cai, Yusu Wang
Graph Neural Networks (GNNs) have achieved a lot of success on graph-structured data. However, it is observed that the performance of graph neural networks does not improve as the…
Understanding the Power of Persistence Pairing via Permutation Test
Chen Cai, Yusu Wang
Recently many efforts have been made to incorporate persistence diagrams, one of the major tools in topological data analysis (TDA), into machine learning pipelines. To better unde…
A Topological Regularizer for Classifiers via Persistent Homology
Chao Chen, Xiuyan Ni, Qinxun Bai +1
Regularization plays a crucial role in supervised learning. Most existing methods enforce a global regularization in a structure agnostic manner. In this paper, we initiate a new d…