2 citations · 2 across the 10 of their papers we have counts for
11 papers
Classical and quantum spectral density estimation under local graph access
Rong-Hua Li, Meihao Liao, Yichun Yang
We study spectral density estimation for the normalized adjacency matrix of an unweighted graph under local access model. Previously, Cohen-Steiner et al. [KDD 2018] proposed an al…
Theoretically and Practically Efficient Resistance Distance Computation on Large Graphs
Yichun Yang, Longlong Lin, Rong-Hua Li +2
The computation of resistance distance is pivotal in a wide range of graph analysis applications, including graph clustering, link prediction, and graph neural networks. Despite it…
BD-Index: Scalable Biharmonic Distance Queries on Large Graphs via Divide-and-Conquer Indexing
Yueyang Pan, Meihao Liao, Rong-Hua Li
Biharmonic distance (\bd) is a powerful graph distance metric with many applications, including identifying critical links in road networks and mitigating over-squashing problem in…
Scalable and Provable Kemeny Constant Computation on Static and Dynamic Graphs: A 2-Forest Sampling Approach
Cheng Li, Meihao Liao, Rong-Hua Li +1
Kemeny constant, defined as the expected hitting time of random walks from a source node to a randomly chosen target node, is a fundamental metric in graph data management with man…
Efficient Exact Resistance Distance Computation on Small-Treewidth Graphs: a Labelling Approach
Meihao Liao, Yueyang Pan, Rong-Hua Li +1
Resistance distance computation is a fundamental problem in graph analysis, yet existing random walk-based methods are limited to approximate solutions and suffer from poor efficie…
Improved Algorithms for Effective Resistance Computation on Graphs
Yichun Yang, Rong-Hua Li, Meihao Liao +1
Effective Resistance (ER) is a fundamental tool in various graph learning tasks. In this paper, we address the problem of efficiently approximating ER on a graph $\mathcal{G}=(\mat…
Scaling Up Graph Propagation Computation on Large Graphs: A Local Chebyshev Approximation Approach
Yichun Yang, Rong-Hua Li, Meihao Liao +2
Graph propagation (GP) computation plays a crucial role in graph data analysis, supporting various applications such as graph node similarity queries, graph node ranking, graph clu…
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
Xunkai Li, Meihao Liao, Zhengyu Wu +4
Most existing graph neural networks (GNNs) are limited to undirected graphs, whose restricted scope of the captured relational information hinders their expressive capabilities and…
Scalable Algorithms for Laplacian Pseudo-inverse Computation
Meihao Liao, Rong-Hua Li, Qiangqiang Dai +2
The pseudo-inverse of a graph Laplacian matrix, denoted as , finds extensive application in various graph analysis tasks. Notable examples include the calculation of ele…
Privacy-Preserving Graph Embedding based on Local Differential Privacy
Zening Li, Rong-Hua Li, Meihao Liao +2
Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks,…
Scaling Up Maximal k-plex Enumeration
Qiangqiang Dai, Rong-Hua Li, Hongchao Qin +2
Finding all maximal -plexes on networks is a fundamental research problem in graph analysis due to many important applications, such as community detection, biological graph ana…