b'Yusu Wang'

research

∙ 02/16/2023

Distances for Markov Chains, and Their Differentiation

(Directed) graphs with node attributes are a common type of data in vari...

0 Tristan Brugère, et al. ∙

research

∙ 02/14/2023

Understanding Oversquashing in GNNs through the Lens of Effective Resistance

Message passing graph neural networks are popular learning architectures...

0 Mitchell Black, et al. ∙

research

∙ 02/07/2023

A generalization of the persistent Laplacian to simplicial maps

The graph Laplacian is a fundamental object in the analysis of and optim...

0 Aziz Burak Gülen, et al. ∙

research

∙ 02/01/2023

The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs

In this paper, we present a novel interpretation of the so-called Weisfe...

0 Samantha Chen, et al. ∙

research

∙ 01/27/2023

On the Connection Between MPNN and Graph Transformer

Graph Transformer (GT) recently has emerged as a new paradigm of graph l...

0 Chen Cai, et al. ∙

research

∙ 01/06/2023

Principal Component Analysis in Space Forms

Principal component analysis (PCA) is a workhorse of modern data science...

0 Puoya Tabaghi, et al. ∙

research

∙ 11/07/2022

Implicit Graphon Neural Representation

Graphons are general and powerful models for generating graphs of varyin...

0 Xinyue Xia, et al. ∙

research

∙ 10/31/2022

The Numerical Stability of Hyperbolic Representation Learning

Given the exponential growth of the volume of the ball w.r.t. its radius...

0 Gal Mishne, et al. ∙

research

∙ 10/21/2022

Learning Ultrametric Trees for Optimal Transport Regression

Optimal transport provides a metric which quantifies the dissimilarity b...

0 Samantha Chen, et al. ∙

research

∙ 06/06/2022

On the Convergence of Optimizing Persistent-Homology-Based Losses

Topological loss based on persistent homology has shown promise in vario...

5 Yikai Zhang, et al. ∙

research

∙ 02/05/2022

Weisfeiler-Lehman meets Gromov-Wasserstein

The Weisfeiler-Lehman (WL) test is a classical procedure for graph isomo...

0 Samantha Chen, et al. ∙

research

∙ 01/28/2022

Neural Approximation of Extended Persistent Homology on Graphs

Persistent homology is a widely used theory in topological data analysis...

8 Zuoyu Yan, et al. ∙

research

∙ 01/25/2022

Convergence of Invariant Graph Networks

Although theoretical properties such as expressive power and over-smooth...

9 Chen Cai, et al. ∙

research

∙ 09/15/2021

Graph skeletonization of high-dimensional point cloud data via topological method

Geometric graphs form an important family of hidden structures behind da...

0 Lucas Magee, et al. ∙

research

∙ 04/15/2021

Approximation algorithms for 1-Wasserstein distance between persistence diagrams

Recent years have witnessed a tremendous growth using topological summar...

0 Samantha Chen, et al. ∙

research

∙ 04/12/2021

Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph

We present a SE(3)-equivariant graph neural network (GNN) approach that ...

0 Chen Cai, et al. ∙

research

∙ 03/18/2021

Topology-Aware Segmentation Using Discrete Morse Theory

In the segmentation of fine-scale structures from natural and biomedical...

10 Xiaoling Hu, et al. ∙

research

∙ 02/02/2021

Graph Coarsening with Neural Networks

As large-scale graphs become increasingly more prevalent, it poses signi...

0 Chen Cai, et al. ∙

research

∙ 12/04/2020

Persistent Laplacians: properties, algorithms and implications

The combinatorial graph Laplacian has been a fundamental object in the a...

0 Facundo Mémoli, et al. ∙

research

∙ 11/11/2020

Ordinally Consensus Subset over Multiple Metrics

In this paper, we propose to study the following maximum ordinal consens...

0 Dingkang Wang, et al. ∙

research

∙ 06/23/2020

A Note on Over-Smoothing for Graph Neural Networks

Graph Neural Networks (GNNs) have achieved a lot of success on graph-str...

0 Chen Cai, et al. ∙

research

∙ 03/20/2020

Detection and skeletonization of single neurons and tracer injections using topological methods

Neuroscientific data analysis has traditionally relied on linear algebra...

0 Dingkang Wang, et al. ∙

research

∙ 03/10/2020

Elder-Rule-Staircodes for Augmented Metric Spaces

An augmented metric space is a metric space (X, d_X) equipped with a fun...

0 Chen Cai, et al. ∙

research

∙ 01/27/2020

An efficient algorithm for 1-dimensional (persistent) path homology

This paper focuses on developing an efficient algorithm for analyzing a ...

0 Tamal K. Dey, et al. ∙

research

∙ 01/16/2020

Understanding the Power of Persistence Pairing via Permutation Test

Recently many efforts have been made to incorporate persistence diagrams...

0 Chen Cai, et al. ∙

research

∙ 11/01/2019

A limit theorem for the 1st Betti number of layer-1 subgraphs in random graphs

We initiate the study of local topology of random graphs. The high level...

0 Minghao Tian, et al. ∙

research

∙ 09/15/2019

Road Network Reconstruction from Satellite Images with Machine Learning Supported by Topological Methods

Automatic Extraction of road network from satellite images is a goal tha...

21 Tamal K. Dey, et al. ∙

research

∙ 07/31/2019

A Structural Average of Labeled Merge Trees for Uncertainty Visualization

Physical phenomena in science and engineering are frequently modeled usi...

0 Lin Yan, et al. ∙

research

∙ 07/31/2019

Intrinsic Interleaving Distance for Merge Trees

Merge trees are a type of graph-based topological summary that tracks th...

0 Ellen Gasparovic, et al. ∙

research

∙ 04/27/2019

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

Recently a new feature representation and data analysis methodology base...

0 Qi Zhao, et al. ∙

research

∙ 03/20/2019

Local Versus Global Distances for Zigzag Persistence Modules

This short note establishes explicit and broadly applicable relationship...

0 Ellen Gasparovic, et al. ∙

research

∙ 12/13/2018

The Relationship Between the Intrinsic Cech and Persistence Distortion Distances for Metric Graphs

Metric graphs are meaningful objects for modeling complex structures tha...

0 Ellen Gasparovic, et al. ∙

research

∙ 12/06/2018

An Improved Cost Function for Hierarchical Cluster Trees

Hierarchical clustering has been a popular method in various data analys...

0 Dingkang Wang, et al. ∙

research

∙ 12/06/2018

A New Cost Function for Hierarchical Cluster Trees

Hierarchical clustering has been a popular method in various data analys...

0 Dingkang Wang, et al. ∙

research

∙ 11/08/2018

A simple yet effective baseline for non-attribute graph classification

Graphs are complex objects that do not lend themselves easily to typical...

0 Chen Cai, et al. ∙

research

∙ 11/06/2018

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

Gromov-Hausdorff (GH) distance is a natural way to measure the distortio...

0 Elena Farahbakhsh Touli, et al. ∙

research

∙ 10/19/2018

Local cliques in ER-perturbed random geometric graphs

Random graphs are mathematical models that have applications in a wide r...

0 Matthew Kahle, et al. ∙

research

∙ 06/27/2018

A Topological Regularizer for Classifiers via Persistent Homology

Regularization plays a crucial role in supervised learning. Most existin...

0 Chao Chen, et al. ∙

research

∙ 06/27/2018

TopoReg: A Topological Regularizer for Classifiers

Regularization plays a crucial role in supervised learning. A successful...

0 Chao Chen, et al. ∙

research

∙ 05/14/2018

Topological Skeletonization and Tree-Summarization of Neurons Using Discrete Morse Theory

Neuroscientific data analysis has classically involved methods for stati...

0 Suyi Wang, et al. ∙

research

∙ 03/14/2018

Graph Reconstruction by Discrete Morse Theory

Recovering hidden graph-like structures from potentially noisy data is a...

0 Tamal K. Dey, et al. ∙

research

∙ 01/21/2018

Efficient algorithms for computing a minimal homology basis

Efficient computation of shortest cycles which form a homology basis und...

0 Tamal K. Dey, et al. ∙

research

∙ 12/18/2017

Vietoris-Rips and Cech Complexes of Metric Gluings

We study Vietoris-Rips and Cech complexes of metric wedge sums and metri...

0 Michal Adamaszek, et al. ∙

research

∙ 06/20/2017

Unperturbed: spectral analysis beyond Davis-Kahan

Classical matrix perturbation results, such as Weyl's theorem for eigenv...

0 Justin Eldridge, et al. ∙

research

∙ 07/06/2016

Graphons, mergeons, and so on!

In this work we develop a theory of hierarchical clustering for graphs. ...

0 Justin Eldridge, et al. ∙

research

∙ 06/21/2015

Beyond Hartigan Consistency: Merge Distortion Metric for Hierarchical Clustering

Hierarchical clustering is a popular method for analyzing data which ass...

0 Justin Eldridge, et al. ∙

research

∙ 11/28/2012

Graph Laplacians on Singular Manifolds: Toward understanding complex spaces: graph Laplacians on manifolds with singularities and boundaries

Recently, much of the existing work in manifold learning has been done u...

0 Mikhail Belkin, et al. ∙

Yusu Wang

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