9 papers
Graph Matching in Random Geometric Graphs I: Efficient Algorithms
Dong Huang, Ankang Jiao, Shuangping Li +1
We study the recovery of the hidden vertex correspondence between two correlated dense random geometric graphs. Each graph is generated from latent unit vectors in $\mathbb R^d…
Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs
Dong Huang, Jianbo Sun, Pengkun Yang
Evaluating large language models (LLMs) is important for understanding their capabilities, comparing competing systems, and supporting the deployment of reliable models in practice…
Attributed Network Alignment: Statistical Limits and Efficient Algorithm
Dong Huang, Chenyang Tian, Pengkun Yang
This paper studies the problem of recovering a hidden vertex correspondence between two correlated graphs when both edge weights and node features are observed. While most existing…
Fundamental Limits of Community Detection in Contextual Multi-Layer Stochastic Block Models
Shuyang Gong, Dong Huang, Zhangsong Li
We consider the problem of community detection from the joint observation of a high-dimensional covariate matrix and sparse networks, all encoding noisy, partial information ab…
Sample Complexity of Correlation Detection in Erdős-Rényi graphs
Dong Huang, Pengkun Yang
In this paper, we study correlation detection problem between a pair of Erdős-Rényi graphs. Under the null hypothesis, the two graphs are independent, whereas under the alternative…
Variational Estimators for Node Popularity Models
Jony Karki, Dongzhou Huang, Yunpeng Zhao
Node popularity is recognized as a key factor in modeling real-world networks, capturing heterogeneity in connectivity across communities. This concept is equally important in bipa…
Testing Correlation in Graphs by Counting Bounded Degree Motifs
Dong Huang, Pengkun Yang
We investigate the problem of detecting correlation between two Erdős-Rényi graphs , formulated as a hypothesis testing problem: under the null hypothesis, the two graphs a…
Sample Complexity of Correlation Detection in the Gaussian Wigner Model
Dong Huang, Pengkun Yang
Correlation analysis is a fundamental step in uncovering meaningful insights from complex datasets. In this paper, we study the problem of detecting correlations between two random…
Information-Theoretic Thresholds for the Alignments of Partially Correlated Graphs
Dong Huang, Xianwen Song, Pengkun Yang
This paper studies the problem of recovering the hidden vertex correspondence between two correlated random graphs. We propose the partially correlated Erdős-Rényi graphs model, wh…