95 citations · 620 across the 56 of their papers we have counts for
8 papers · 1 filter
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian
Alexander Cloninger, Gal Mishne, Andreas Oslandsbotn +3
We investigate the concept of effective resistance in connection graphs, expanding its traditional application from undirected graphs. We propose a robust definition of effective r…
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
Samantha Chen, Yusu Wang
Learning distance functions between complex objects, such as the Wasserstein distance to compare point sets, is a common goal in machine learning applications. However, functions o…
A generalization of the persistent Laplacian to simplicial maps
Aziz Burak Gülen, Facundo Mémoli, Zhengchao Wan +1
The graph Laplacian is a fundamental object in the analysis of and optimization on graphs. This operator can be extended to a simplicial complex and therefore offers a way to p…
Distances for Markov Chains, and Their Differentiation
Tristan Brugère, Zhengchao Wan, Yusu Wang
(Directed) graphs with node attributes are a common type of data in various applications and there is a vast literature on developing metrics and efficient algorithms for comparing…
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
Mitchell Black, Zhengchao Wan, Amir Nayyeri +1
Message passing graph neural networks (GNNs) are a popular learning architectures for graph-structured data. However, one problem GNNs experience is oversquashing, where a GNN has…
The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs
Samantha Chen, Sunhyuk Lim, Facundo Mémoli +2
In this paper, we present a novel interpretation of the so-called Weisfeiler-Lehman (WL) distance, introduced by Chen et al. (2022), using concepts from stochastic processes. The W…