Contrastive Representation Learning Based on Multiple Node-centered Subgraphs
As the basic element of graph-structured data, node has been recognized as the main object of study in graph representation learning. A single node intuitively has multiple node-centered subgraphs from the whole graph (e.g., one person in a social network has multiple social circles based on his different relationships). We study this intuition under the framework of graph contrastive learning, and propose a multiple node-centered subgraphs contrastive representation learning method to learn node representation on graphs in a self-supervised way. Specifically, we carefully design a series of node-centered regional subgraphs of the central node. Then, the mutual information between different subgraphs of the same node is maximized by contrastive loss. Experiments on various real-world datasets and different downstream tasks demonstrate that our model has achieved state-of-the-art results.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningGraph Representation LearningRepresentation LearningSimilar Papers 제목 키워드 기반
NESS: Node Embeddings from Static SubGraphs
We present a framework for learning Node Embeddings from Static Subgraphs (NESS) using a graph autoencoder (GAE) in a transductive setting. NESS is based on two key ideas: i) Partitioning the training graph to multiple s…
Contrastive LearningGraph EmbeddingLink PredictionGenerative Subgraph Contrast for Self-Supervised Graph Representation Learning
Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning methods rely on the vector inner product bas…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation LearningMotif-Driven Contrastive Learning of Graph Representations
Pre-training Graph Neural Networks (GNN) via self-supervised contrastive learning has recently drawn lots of attention. However, most existing works focus on node-level contrastive learning, which cannot capture global g…
ClusteringContrastive LearningSelf-Supervised LearningAdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph Training
Training graph neural networks (GNNs) with good generalizability on large-scale graphs is a challenging problem. Existing methods mainly divide the input graph into multiple subgraphs and train them in different batches …
Contrastive LearningData AugmentationNode ClassificationLocal Structure-aware Graph Contrastive Representation Learning
Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackle the label problem effectively, mainl…
Contrastive LearningGraph Neural NetworkGraph Representation LearningLink Prediction+2