36 citations · 77 across the 6 of their papers we have counts for
6 papers
Hierarchical Transformer for Scalable Graph Learning
Wenhao Zhu, Tianyu Wen, Guojie Song +2
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning…
Homophily-oriented Heterogeneous Graph Rewiring
Jiayan Guo, Lun Du, Wendong Bi +6
With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potenti…
LEReg: Empower Graph Neural Networks with Local Energy Regularization
Xiaojun Ma, Hanyue Chen, Guojie Song
Researches on analyzing graphs with Graph Neural Networks (GNNs) have been receiving more and more attention because of the great expressive power of graphs. GNNs map the adjacency…
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily
Xiaojun Ma, Qin Chen, Yuanyi Ren +2
Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream…
GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily
Lun Du, Xiaozhou Shi, Qiang Fu +4
Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine learning tasks. For node-level tasks, GNNs have strong power to model the homophily property of gra…
EPNE: Evolutionary Pattern Preserving Network Embedding
Junshan Wang, Yilun Jin, Guojie Song +1
Information networks are ubiquitous and are ideal for modeling relational data. Networks being sparse and irregular, network embedding algorithms have caught the attention of many…