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. In this work, we propose RPGNN, a novel relation prediction model based on the graph neural network (GNN) and multi-task learning to solve this problem. Existing GNN models for HIN representation learning usually focus on the node classification/clustering task. They require the type information of all edges and nodes and always learn a weight matrix for each type, thus requiring a large number of learning parameters on HINs with rich schema. In contrast, our model directly encodes and learns relations in HIN and avoids the requirement of type information during message passing in GNN. Hence, our model is more robust to the missing types for the relation prediction task on HINs. The experiments on real HINs show that our model can consistently achieve better performance than several state-of-the-art HIN representation learning methods.
|Title of host publication||CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|Publication status||Published - 2021 Oct 26|
|Event||30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia|
Duration: 2021 Nov 1 → 2021 Nov 5
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||30th ACM International Conference on Information and Knowledge Management, CIKM 2021|
|Period||21/11/1 → 21/11/5|
Bibliographical noteFunding Information:
Xin Cao is supported by ARC DE190100663. Yixiang Fang is supported by CUHK-SZ grant UDF01002139. Won-Yong Shin is supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C3004345). Yu Hao is the corresponding author of this paper.
© 2021 ACM.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Decision Sciences(all)