most citedNot All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2022★ 2 cited

Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning

Tiantian He, Haicang Zhou, Yew-Soon Ong +1

Graph attention networks (GATs) are powerful tools for analyzing graph data from various real-world scenarios. To learn representations for downstream tasks, GATs generally attend…

cs.LG2020

SCE: Scalable Network Embedding from Sparsest Cut

Shengzhong Zhang, Zengfeng Huang, Haicang Zhou +1

Large-scale network embedding is to learn a latent representation for each node in an unsupervised manner, which captures inherent properties and structural information of the unde…