9 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Device-to-Device Communications Enabled Multicast Scheduling with the Multi-Level Codebook in mmWave Small Cells
Yong Niu, Liren Yu, Yong Li +3
With the exponential growth of mobile data, there are increasing interests to deploy small cells in millimeter wave (mmWave) bands to underlay the conventional homogeneous macrocel…