Deep Graph Contrastive Representation Learning

7 Jun 2020  ·  Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang ·

Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective at the node level. Specifically, we generate two graph views by corruption and learn node representations by maximizing the agreement of node representations in these two views. To provide diverse node contexts for the contrastive objective, we propose a hybrid scheme for generating graph views on both structure and attribute levels. Besides, we provide theoretical justification behind our motivation from two perspectives, mutual information and the classical triplet loss. We perform empirical experiments on both transductive and inductive learning tasks using a variety of real-world datasets. Experimental experiments demonstrate that despite its simplicity, our proposed method consistently outperforms existing state-of-the-art methods by large margins. Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Node Classification Citeseer GRACE Accuracy 72.1 ± 0.5 # 47
Node Classification Cora GRACE Accuracy 83.3% ± 0.4% # 41
Node Classification DBLP GRACE Accuracy 84.2 ± 0.1 # 1
Node Classification PPI GRACE F1 66.2 # 20
Micro-F1 66.2 # 1
Node Classification Pubmed GRACE Accuracy 86.7 ± 0.1 # 19
Node Classification Reddit GRACE Micro-F1 94.2 ± 0.0 # 1


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