95 citations · 620 across the 56 of their papers we have counts for
9 papers · 1 filter
An Improved Cost Function for Hierarchical Cluster Trees
Dingkang Wang, Yusu Wang
Hierarchical clustering has been a popular method in various data analysis applications. It partitions a data set into a hierarchical collection of clusters, and can provide a glob…
The Relationship Between the Intrinsic Cech and Persistence Distortion Distances for Metric Graphs
Ellen Gasparovic, Maria Gommel, Emilie Purvine +4
Metric graphs are meaningful objects for modeling complex structures that arise in many real-world applications, such as road networks, river systems, earthquake faults, blood vess…
A simple yet effective baseline for non-attributed graph classification
Chen Cai, Yusu Wang
Graphs are complex objects that do not lend themselves easily to typical learning tasks. Recently, a range of approaches based on graph kernels or graph neural networks have been d…
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…
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…
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…