Relation Prediction via Graph Neural Network in Heterogeneous Information Networks with Missing Type Information
Han Zhang, Hao Yu, Xin Cao, Yixiang Fang, Won-Yong Shin, Wei Wang · 2021
Relation prediction is a fundamental task in network analysis which aims to predict the relationship between two nodes. Thus, this differes from the traditional link prediction problem predicting whether a link exists between a pair of nodes, which can be viewed as a binary classification task. However, in the heterogeneous information network (HIN) which contains multiple types of nodes and multiple relations between nodes, the relation prediction task is more challenging. In addition, the HIN might have missing relation types on some edges and missing node types on some nodes, which makes the problem even harder.