9 citations · 13 across the 3 of their papers we have counts for
3 papers · 1 filter
Road Network Representation Learning with the Third Law of Geography
Haicang Zhou, Weiming Huang, Yile Chen +3
Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we id…
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…
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…