activity
20202026
most citedTesting network correlation efficiently via counting trees

17 citations · 33 across the 6 of their papers we have counts for

collaborators

6 papers

math.PR2026

Flexibility allocation in random bipartite matching markets: exact matching rates and dominance regimes

Taha Ameen, Flore Sentenac, Sophie H. Yu

This paper studies how a fixed flexibility budget should be allocated across the two sides of a balanced bipartite matching market. We model compatibilities via a sparse bipartite…

math.ST2026

Detection of local geometry in random graphs: information-theoretic and computational limits

Jinho Bok, Shuangping Li, Sophie H. Yu

We study the problem of detecting local geometry in random graphs. We introduce a model , where a hidden community of average size has edges drawn as a…

cs.DS2022★ 1 cited

Random graph matching at Otter's threshold via counting chandeliers

Cheng Mao, Yihong Wu, Jiaming Xu +1

We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex. For two Erdős-Ré…

math.ST2021★ 17 cited

Testing network correlation efficiently via counting trees

Cheng Mao, Yihong Wu, Jiaming Xu +1

We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence. The test statistic is based on counting the co-occurrence…

math.ST2021★ 5 cited

Settling the Sharp Reconstruction Thresholds of Random Graph Matching

Yihong Wu, Jiaming Xu, Sophie H. Yu

This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are com…

math.ST2020★ 10 cited

Testing correlation of unlabeled random graphs

Yihong Wu, Jiaming Xu, Sophie H. Yu

We study the problem of detecting the edge correlation between two random graphs with unlabeled nodes. This is formalized as a hypothesis testing problem, where under the null…