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20122023
most citedA Note on Over-Smoothing for Graph Neural Networks

95 citations · 217 across the 25 of their papers we have counts for

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13 papers · 1 filter

cs.CG20216 cited

Approximation algorithms for 1-Wasserstein distance between persistence diagrams

Samantha Chen, Yusu Wang

Recent years have witnessed a tremendous growth using topological summaries, especially the persistence diagrams (encoding the so-called persistent homology) for analyzing complex…

cs.CG2020

An efficient algorithm for -dimensional (persistent) path homology

Tamal K. Dey, Tianqi Li, Yusu Wang

This paper focuses on developing an efficient algorithm for analyzing a directed network (graph) from a topological viewpoint. A prevalent technique for such topological analysis i…

cs.CG2019

A Structural Average of Labeled Merge Trees for Uncertainty Visualization

Lin Yan, Yusu Wang, Elizabeth Munch +2

Physical phenomena in science and engineering are frequently modeled using scalar fields. In scalar field topology, graph-based topological descriptors such as merge trees, contour…

cs.CG2019

Learning metrics for persistence-based summaries and applications for graph classification

Qi Zhao, Yusu Wang

Recently a new feature representation and data analysis methodology based on a topological tool called persistent homology (and its corresponding persistence diagram summary) has s…

cs.CG2018

FPT-algorithms for computing Gromov-Hausdorff and interleaving distances between trees

Elena Farahbakhsh Touli, Yusu Wang

Gromov-Hausdorff (GH) distance is a natural way to measure the distortion between two metric spaces. However, there has been only limited algorithmic development to compute or appr…

cs.CG2018

Local cliques in ER-perturbed random geometric graphs

Matthew Kahle, Minghao Tian, Yusu Wang

Random graphs are mathematical models that have applications in a wide range of domains. We study the following model where one adds Erdős--Rényi (ER) type perturbation to a random…