GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (2006.09963v3)

Abstract: Graph representation learning has emerged as a powerful technique for addressing real-world problems. Various downstream graph learning tasks have benefited from its recent developments, such as node classification, similarity search, and graph classification. However, prior arts on graph representation learning focus on domain specific problems and train a dedicated model for each graph dataset, which is usually non-transferable to out-of-domain data. Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- a self-supervised graph neural network pre-training framework -- to capture the universal network topological properties across multiple networks. We design GCC's pre-training task as subgraph instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations. We conduct extensive experiments on three graph learning tasks and ten graph datasets. The results show that GCC pre-trained on a collection of diverse datasets can achieve competitive or better performance to its task-specific and trained-from-scratch counterparts. This suggests that the pre-training and fine-tuning paradigm presents great potential for graph representation learning.

Graph Contrastive Coding for Graph Neural Network Pre-Training: A Summary

In "GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training," Qiu et al. introduce a self-supervised learning framework aimed at pre-training graph neural networks (GNNs) to capture and transfer structural representations across various graphs. Their main contribution is the development of Graph Contrastive Coding (GCC), a methodology that leverages contrastive learning mechanisms to discern topological structures within and across different graph datasets.

Core Contributions

The paper addresses the inherent limitations of existing graph representation learning models, which have traditionally been domain-specific and non-transferable. It moves towards a more generalized approach, inspired by the success of pre-training paradigms in other fields, such as NLP and computer vision (CV).

Key insights and contributions include:

1. Instance Discrimination as a Pre-Training Task: GCC employs subgraph instance discrimination to generate self-supervised learning signals. Each subgraph instance is treated as a distinct class, and the network learns to differentiate between these instances.
2. Graph Sampling and Augmentation: By utilizing random walks with restart (RWR), subgraph induction, and anonymization, GCC effectively samples subgraphs that maintain structural integrity while masking specific vertex identities.
3. Contrastive Learning Mechanisms: GCC adopts the InfoNCE loss and implements both end-to-end (E2E) and momentum contrast (MoCo) mechanisms for maintaining large-scale dictionaries necessary for efficient contrastive learning.
4. Generalized Positional Embedding: The use of top eigenvectors from the subgraph's normalized Laplacian matrix as vertex features bridges the gap for structural representation learning in the absence of explicit node attributes.

Experimental Evaluation

The authors validate the performance and transferability of GCC through extensive experiments across three distinct graph learning tasks: node classification, graph classification, and similarity search. The pre-training is conducted on a diverse set of graphs including Academia, DBLP (from SNAP and NetRep), Facebook, IMDB, and LiveJournal datasets.

1. Node Classification: GCC shows competitive performance against baselines like GraphWave and Struc2vec on the US-Airport and H-index datasets. This is significant because GCC pre-training does not utilize any target domain-specific information during training.
2. Graph Classification: GCC outperforms DGK, graph2vec, and performs comparably to GIN in datasets such as IMDB-Binary, IMDB-Multi, COLLAB, Reddit-Binary, and Reddit-Multi5K. The robustness and fine-tuning capability of pre-trained GCC make it versatile for graph-level tasks.
3. Top-$k$ Similarity Search: In tasks involving co-author networks such as KDD-ICDM and SIGIR-CIKM, GCC demonstrates its effectiveness, performing better than models like RolX and Panther++.

Implications and Future Directions

The implication of this research is multi-fold. From a practical standpoint, the ability to pre-train a GNN that captures universal structural patterns ensures adaptability across various domains without the need for domain-specific customization. Theoretically, it raises tantalizing questions about the existence and nature of universal patterns in complex networks.

Future developments could include:

• Exploring Other Datasets: Benchmarking on an even broader range of datasets, including bioinformatics and social media graphs, would offer further evidence of GCC’s transferability.
• Fine-Grained Structural Understanding: Further research could refine the types of structural patterns GCC is sensitive to, potentially enhancing its discriminative power.
• Integration with Domain-Specific Attributes: Advancing GCC to incorporate node and edge attributes could bridge the gap between purely structural and feature-based learning, potentially opening up new avenues for application in more complex and dynamic graph settings.

In summary, Qiu et al.'s GCC framework represents a significant step towards generalizable and transferable graph neural network models, opening up new possibilities for applying GNNs across disparate domains while maintaining high performance.