10.1k citations · 17.4k across the 69 of their papers we have counts for
7 papers · 1 filter
Domain-adaptive Graph Attention-supervised Network for Cross-network Edge Classification
Xiao Shen, Mengqiu Shao, Shirui Pan +2
Graph neural networks (GNNs) have shown great ability in modeling graphs, however, their performance would significantly degrade when there are noisy edges connecting nodes from di…
Neighbor Contrastive Learning on Learnable Graph Augmentation
Xiao Shen, Dewang Sun, Shirui Pan +2
Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt h…
VR-GNN: Variational Relation Vector Graph Neural Network for Modeling both Homophily and Heterophily
Fengzhao Shi, Ren Li, Yanan Cao +5
Graph Neural Networks (GNNs) have achieved remarkable success in diverse real-world applications. Traditional GNNs are designed based on homophily, which leads to poor performance…
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
Xu Yan, Xiaoliang Fan, Peizhen Yang +5
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existi…
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
Di Jin, Zhizhi Yu, Pengfei Jiao +5
Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection ha…
Multivariate Relations Aggregation Learning in Social Networks
Jin Xu, Shuo Yu, Ke Sun +4
Multivariate relations are general in various types of networks, such as biological networks, social networks, transportation networks, and academic networks. Due to the principle…
Universal Network Representation for Heterogeneous Information Networks
Ruiqi Hu, Celina Ping Yu, Sai-Fu Fung +3
Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture co…