3 papers
cs.LG2024
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…
cs.LG2024
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…
cs.DS2023
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…