9 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.LG2022
SeedGNN: Graph Neural Networks for Supervised Seeded Graph Matching
Liren Yu, Jiaming Xu, Xiaojun Lin
There is a growing interest in designing Graph Neural Networks (GNNs) for seeded graph matching, which aims to match two unlabeled graphs using only topological information and a s…
cs.DS2021★ 2 cited
The Power of -hops in Matching Power-Law Graphs
Liren Yu, Jiaming Xu, Xiaojun Lin
This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law deg…
cs.DS2020★ 9 cited
Graph Matching with Partially-Correct Seeds
Liren Yu, Jiaming Xu, Xiaojun Lin
Graph matching aims to find the latent vertex correspondence between two edge-correlated graphs and has found numerous applications across different fields. In this paper, we study…