519 citations · 1.3k across the 18 of their papers we have counts for
6 papers · 1 filter
Learning Node Representations from Noisy Graph Structures
Junshan Wang, Ziyao Li, Qingqing Long +3
Learning low-dimensional representations on graphs has proved to be effective in various downstream tasks. However, noises prevail in real-world networks, which compromise networks…
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Xiao Wang, Deyu Bo, Chuan Shi +3
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representat…
Heterogeneous Graph Neural Network for Recommendation
Jinghan Shi, Houye Ji, Chuan Shi +3
The prosperous development of e-commerce has spawned diverse recommendation systems. As a matter of fact, there exist rich and complex interactions among various types of nodes in…
Relation Structure-Aware Heterogeneous Information Network Embedding
Yuanfu Lu, Chuan Shi, Linmei Hu +1
Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heteroge…
Heterogeneous Graph Attention Network
Xiao Wang, Houye Ji, Chuan Shi +4
Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it…
Heterogeneous Information Network Embedding for Recommendation
Chuan Shi, Binbin Hu, Wayne Xin Zhao +1
Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recomme…