58 citations · 79 across the 5 of their papers we have counts for
5 papers
Fully Hyperbolic Graph Convolution Network for Recommendation
Liping Wang, Fenyu Hu, Shu Wu +1
Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perfor…
Label-informed Graph Structure Learning for Node Classification
Liping Wang, Fenyu Hu, Shu Wu +1
Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this prob…
Graph Classification by Mixture of Diverse Experts
Fenyu Hu, Liping Wang, Shu Wu +2
Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imb…
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu +3
Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convoluti…
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Fenyu Hu, Yanqiao Zhu, Shu Wu +2
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation a…