2022/01/10 by Minjae Park, Park, Minjae
Computer Science · #Advanced Graph Neural Networks #Recommender Systems and Techniques #Text and Document Classification Technologies #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2201.03340
arxiv created 2022/01/11 · arxiv updated 2022/01/12
Heterogeneous graph neural networks can represent information of heterogeneous graphs with excellent ability. Recently, self-supervised learning manner is researched which learns the unique expression of a graph through a contrastive learning method. In the absence of labels, this learning methods show great potential. However, contrastive learning relies heavily on positive and negative pairs, and generating high-quality pairs from heterogeneous graphs is difficult. In this paper, in line with recent innovations in self-supervised learning called BYOL or bootstrapping, we introduce a that can generate good representations without generating large number of pairs. In addition, paying attention to the fact that heterogeneous graphs can be viewed from two perspectives, network schema and meta-path views, high-level expressions in the graphs are captured and expressed. The proposed model showed state-of-the-art performance than other methods in various real world datasets.