When Does Self-Supervision Help Graph Convolutional Networks? (2006.09136v4)

Abstract: Self-supervision as an emerging technique has been employed to train convolutional neural networks (CNNs) for more transferrable, generalizable, and robust representation learning of images. Its introduction to graph convolutional networks (GCNs) operating on graph data is however rarely explored. In this study, we report the first systematic exploration and assessment of incorporating self-supervision into GCNs. We first elaborate three mechanisms to incorporate self-supervision into GCNs, analyze the limitations of pretraining & finetuning and self-training, and proceed to focus on multi-task learning. Moreover, we propose to investigate three novel self-supervised learning tasks for GCNs with theoretical rationales and numerical comparisons. Lastly, we further integrate multi-task self-supervision into graph adversarial training. Our results show that, with properly designed task forms and incorporation mechanisms, self-supervision benefits GCNs in gaining more generalizability and robustness. Our codes are available at https://github.com/Shen-Lab/SS-GCNs.

Analyzing the Role of Self-Supervision in Enhancing Graph Convolutional Networks

The paper "When Does Self-Supervision Help Graph Convolutional Networks?" provides a thorough examination of the integration of self-supervised learning techniques into Graph Convolutional Networks (GCNs). It addresses key questions regarding the performance benefits, the importance of task design, and the impact on adversarial robustness when self-supervision is introduced to GCNs. The research presents a systematic exploration into this area, highlighting mechanisms and tasks that serve as data-driven regularizers to enhance both the generalizability and resilience of GCNs.

Key Contributions

The paper makes several notable contributions:

1. Mechanisms for Incorporation: The authors explore three mechanisms to integrate self-supervision into GCNs: pretraining & finetuning, self-training, and multi-task learning. Among these, multi-task learning is identified as the most effective mechanism, where self-supervised tasks act as regularizers during network training.
2. Novel Tasks Design: Three self-supervised tasks specific to graph data are proposed: node clustering, graph partitioning, and graph completion. These tasks leverage graph structure and node attributes to provide generalized priors that act as regularizations, thereby improving model performance beyond traditional training methods.
3. Adversarial Robustness: The incorporation of self-supervised learning into adversarial training settings was further investigated. It was found that self-supervision contributes to improved robustness against various adversarial attacks such as those targeting node features and graph links, without necessitating larger model architectures or additional data.

Numerical Results and Analysis

The paper presents a series of rigorous experiments. By leveraging multi-task learning, the integration of self-supervised tasks led to consistent improvements in classification performance across common benchmarks such as Cora, Citeseer, and PubMed. Graph partitioning, in particular, was highlighted for its effectiveness across several architectures, including GCN, GAT, and GIN, indicating its broad applicability independent of the underlying graph neural network framework.

Moreover, this paper discusses the effects of varying self-supervised tasks on different graph neural network architectures, illustrating that the benefits can be architecture-dependent. For instance, while GMNN and GraphMix models already embody certain structural and dataset-driven priors, improvements through self-supervision were less pronounced compared to architectures like GCN.

The research further demonstrates the additional robustness conferred by multi-task self-supervision against graph attacks. Tasks like graph completion, which offer combined feature and structure perturbation priors, notably enhanced the resistance against comprehensive link and feature attacks.

Implications and Future Directions

The implications of this research are significant for the deployment of GCNs in real-world applications, where data labeling is sparse, and robustness against adversarial manipulation is crucial. The findings suggest that carefully designed self-supervised tasks can substantially mitigate the need for labeled data and improve model reliability, presenting a viable avenue for enhancing graph-based learning systems.

Future research could explore the extension of these results to more complex and diverse graph datasets, potentially uncovering universal self-supervised tasks that yield consistent benefits. Furthermore, analyzing these mechanisms in fully supervised learning settings could determine the limits of self-supervision in high-label scenarios, closing the loop on understanding the scope and utility of self-supervised learning in graph neural networks.