95 citations · 620 across the 56 of their papers we have counts for
9 papers · 1 filter
Implicit Graphon Neural Representation
Xinyue Xia, Gal Mishne, Yusu Wang
Graphons are general and powerful models for generating graphs of varying size. In this paper, we propose to directly model graphons using neural networks, obtaining Implicit Graph…
The Numerical Stability of Hyperbolic Representation Learning
Gal Mishne, Zhengchao Wan, Yusu Wang +1
Given the exponential growth of the volume of the ball w.r.t. its radius, the hyperbolic space is capable of embedding trees with arbitrarily small distortion and hence has receive…
Learning Ultrametric Trees for Optimal Transport Regression
Samantha Chen, Puoya Tabaghi, Yusu Wang
Optimal transport provides a metric which quantifies the dissimilarity between probability measures. For measures supported in discrete metric spaces, finding the optimal transport…
On the clique number of noisy random geometric graphs
Matthew Kahle, Minghao Tian, Yusu Wang
Let be a random geometric graph, and then for we construct a "-perturbed noisy random geometric graph" where each existing edge in is…
On the Convergence of Optimizing Persistent-Homology-Based Losses
Yikai Zhang, Jiachen Yao, Yusu Wang +1
Topological loss based on persistent homology has shown promise in various applications. A topological loss enforces the model to achieve certain desired topological property. Desp…
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…