Jiangtao Cui

Interpretable and Efficient Heterogeneous Graph Convolutional Network

2021 · IEEE Transactions on Knowledge and Data Engineering

Graph Convolutional Network (GCN) has achieved extraordinary success in learning representations of nodes in graphs. However, regarding Heterogeneous Information Network (HIN), existing HIN-oriented GCN methods still suffer from two deficiencies: (1) they cannot flexibly explore …

Aligning Multiple Knowledge Graphs in A Single Pass

2026

Entity alignment (EA) is to identify equivalent entities across different knowledge graphs (KGs), which can help fuse these KGs into a more comprehensive one. Previous EA methods mainly focus on aligning a pair of KGs, …