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cs.LG2026
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…
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…