Deep Graph Contrastive Representation Learning (2006.04131v2)

Abstract: 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.

• The paper presents GRACE, which learns unsupervised node representations by maximizing agreement between dual corrupted graph views.
• It employs edge removal and feature masking to create diverse graph views, enhancing the effectiveness of node-level contrastive learning.
• Empirical evaluations show significant gains, with GRACE achieving 72.1% accuracy on Citeseer and a 94.2% micro-F1 score on Reddit.

Analysis of Deep Graph Contrastive Representation Learning

The paper "Deep Graph Contrastive Representation Learning" introduces a new framework for unsupervised node representation learning in graph-structured data, building on the foundational concepts of contrastive learning. This development presents a significant step in graph representation learning, leveraging the strengths of Graph Neural Networks (GNNs) and contrastive objectives.

Framework Overview

The proposed approach, GRACE (Graph Contrastive Representation Learning), operates by creating two graph views through corruption techniques and subsequently learning node representations by maximizing the agreement between these views. This method is innovative in its pursuit of contrastive objectives at the node level, diverging from previous works that typically focus on global context leveraging approaches.

Methodology

The framework presents a dual strategy for graph corruption:

• Removing Edges (RE): Randomly removes a subset of edges in the graph to generate an alternative topology.
• Masking Features (MF): Randomly masks node features, allowing for diverse feature representations in different graph views.

This joint corruption at both structure and attribute levels enhances the model's ability to provide varied contexts for effective contrastive learning.

Theoretical Justification

The paper provides theoretical underpinnings for its approach from two perspectives:

1. Mutual Information Maximization: It establishes that the proposed contrastive objective is a lower bound for mutual information between input features and node representations. This perspective ties the work to established principles in unsupervised learning where maximizing mutual information is a known objective for learning meaningful representations.
2. Connection to Triplet Loss: It also relates the contrastive objective to triplet loss, a well-studied approach in metric learning, highlighting the importance of effectively choosing negative samples for training.

Empirical Evaluation

The authors conducted extensive experiments across six datasets, including both transductive and inductive node classification tasks. Results demonstrate substantial performance gains over existing state-of-the-art methods, including better performance on transductive tasks than some supervised learning approaches.

Strong Numerical Results

The empirical results on transductive tasks show that GRACE achieved notable accuracy improvements over methods like DeepWalk, node2vec, and DGI. For instance, on Citeseer, GRACE reached an accuracy of 72.1%, surpassing DGI’s 68.8%. In inductive tasks, GRACE achieved a micro-F1 score of 94.2% on the Reddit dataset, which is marginally better than DGI's 94.0%.

Implications and Future Directions

The paper positions GRACE as not only a robust unsupervised learning method but also a potential competitor to supervised methods in specific scenarios. The elegance of framing node-level contrastive learning without relying on global context makes it versatile and adaptable for various downstream tasks.

Future research could explore refining the corruption techniques, possibly integrating domain-specific knowledge to further enhance view diversity. Moreover, the approach could be extended to other graph-based domains such as molecular structures or knowledge graphs, opening pathways for innovative applications in drug discovery and recommendation systems.

By melding GNNs with contrastive learning in a novel way, this work contributes significantly to the growing field of graph representation learning, setting a foundation for more nuanced unsupervised methodologies.