For the recommendation domain, most of the existing integrated graph neural network
(GNN)-based architectures have still much focused on encoding the associated extra side …

Network embedding is an effective way to realize the quantitative analysis of large-scale
networks. However, mainstream network embedding models are limited by the manually pre …

Due to the ubiquity of graph-structured data, Graph Neural Network (GNN) have been widely
used in different tasks and domains and good results have been achieved in tasks such as …

In recent years, graph neural network (GNN) has become the main stream for most of recent
researches due to its powers in dealing with complex graph data learning problems …

Recently, similar entity searching over knowledge graph (KG) has gained much attentions
by researchers. However, in rich-semantic KGs with multi-typed entities and relations, also …

Link prediction on heterogeneous information network (HIN) is considered as a challenge
problem due to the complexity and diversity in types of nodes and links. Currently, there are …