Cross-view Self-Supervised Learning on Heterogeneous Graph Neural Network via Bootstrapping
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningGraph Neural NetworkSelf-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
HGOT: Self-supervised Heterogeneous Graph Neural Network with Optimal Transport
Heterogeneous Graph Neural Networks (HGNNs), have demonstrated excellent capabilities in processing heterogeneous information networks. Self-supervised learning on heterogeneous graphs, especially contrastive self-superv…
Graph Neural NetworkNode ClassificationSelf-Supervised LearningSelf-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning
Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised learning manner, whi…
Contrastive LearningGraph Neural NetworkGeometry Contrastive Learning on Heterogeneous Graphs
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data. Meanwhile, most existing representation learning methods embed the…
Contrastive LearningNode ClassificationNode ClusteringRepresentation Learning+1Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requirin…
AttributeContrastive LearningGraph Neural NetworkHomophily-aware Heterogeneous Graph Contrastive Learning
Heterogeneous graph pre-training (HGP) has demonstrated remarkable performance across various domains. However, the issue of heterophily in real-world heterogeneous graphs (HGs) has been largely overlooked. To bridge thi…
Contrastive Learning