35 citations · 41 across the 5 of their papers we have counts for
5 papers
The Expressive Power of Graph Neural Networks: A Survey
Bingxu Zhang, Changjun Fan, Shixuan Liu +4
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoreti…
Multi-modal Entity Alignment in Hyperbolic Space
Hao Guo, Jiuyang Tang, Weixin Zeng +2
Many AI-related tasks involve the interactions of data in multiple modalities. It has been a new trend to merge multi-modal information into knowledge graph(KG), resulting in multi…
Towards Entity Alignment in the Open World: An Unsupervised Approach
Weixin Zeng, Xiang Zhao, Jiuyang Tang +3
Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and qual…
Degree-Aware Alignment for Entities in Tail
Weixin Zeng, Xiang Zhao, Wei Wang +2
Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge.…
Collective Entity Alignment via Adaptive Features
Weixin Zeng, Xiang Zhao, Jiuyang Tang +1
Entity alignment (EA) identifies entities that refer to the same real-world object but locate in different knowledge graphs (KGs), and has been harnessed for KG construction and in…