350 citations · 575 across the 5 of their papers we have counts for
5 papers
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…
Graph-level Neural Networks: Current Progress and Future Directions
Ge Zhang, Jia Wu, Jian Yang +6
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of…
A Comprehensive Survey on Community Detection with Deep Learning
Xing Su, Shan Xue, Fanzhen Liu +9
A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in n…
Deep Learning for Community Detection: Progress, Challenges and Opportunities
Fanzhen Liu, Shan Xue, Jia Wu +6
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and d…
Deep Active Learning for Anchor User Prediction
Anfeng Cheng, Chuan Zhou, Hong Yang +4
Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consi…