Self-supervised Learning on Graphs: Deep Insights and New Direction (2006.10141v1)

Abstract: The success of deep learning notoriously requires larger amounts of costly annotated data. This has led to the development of self-supervised learning (SSL) that aims to alleviate this limitation by creating domain specific pretext tasks on unlabeled data. Simultaneously, there are increasing interests in generalizing deep learning to the graph domain in the form of graph neural networks (GNNs). GNNs can naturally utilize unlabeled nodes through the simple neighborhood aggregation that is unable to thoroughly make use of unlabeled nodes. Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data. Different from data instances in the image and text domains, nodes in graphs present unique structure information and they are inherently linked indicating not independent and identically distributed (or i.i.d.). Such complexity is a double-edged sword for SSL on graphs. On the one hand, it determines that it is challenging to adopt solutions from the image and text domains to graphs and dedicated efforts are desired. On the other hand, it provides rich information that enables us to build SSL from a variety of perspectives. Thus, in this paper, we first deepen our understandings on when, why, and which strategies of SSL work with GNNs by empirically studying numerous basic SSL pretext tasks on graphs. Inspired by deep insights from the empirical studies, we propose a new direction SelfTask to build advanced pretext tasks that are able to achieve state-of-the-art performance on various real-world datasets. The specific experimental settings to reproduce our results can be found in \url{https://github.com/ChandlerBang/SelfTask-GNN}.

• The paper presents a joint training strategy that integrates self-supervised learning with graph neural networks to enhance node classification under limited labeling.
• It evaluates three integration methods—joint training, two-stage training, and task-specific SSL—demonstrating joint training's superior performance on standard benchmarks.
• Innovative SSL tasks like SelfTask-Distance2Labeled and SelfTask-CorrectedLabel are introduced, significantly improving classification accuracy across datasets.

Self-supervised Learning on Graphs: Deep Insights and New Directions

The paper "Self-supervised Learning on Graphs: Deep Insights and New Directions" presents a comprehensive analysis and exploration of integrating self-supervised learning (SSL) with graph neural networks (GNNs) to enhance node classification performance, particularly in scenarios with limited labeled data. These efforts are increasingly relevant given the pronounced applicability of GNNs across varied domains and the enduring challenge of resource-intensive data labeling.

Core Concepts and Methodological Framework

The research pivots around leveraging SSL to compensate for sparse labels in GNNs. The authors classify potential SSL pretext tasks into those drawing on graph structure and attribute information. The complexity of graph-structured data, characterized by the inherent links and non-i.i.d nature of nodes, presents both unique challenges and ample opportunities for SSL task design.

Three methodological strategies are explored for SSL and GNN integration:

1. Joint Training: Concurrently optimizing SSL and task-specific losses.
2. Two-stage Training: Pre-training on SSL tasks followed by fine-tuning on the main task.
3. Task-specific SSL: An emphasis on incorporating label-driven self-supervision.

Preliminary Analysis

Basic SSL tasks designed from graph structure (such as local connectivity, global positioning) and attributes (including feature reconstruction and similarity) were initially assessed. Joint training emerged as particularly effective compared to two-stage training, facilitating more significant performance improvements across benchmark datasets like Cora, Citeseer, and Pubmed.

Insights and New Directions

The empirical analysis implied that while GNNs naturally encode certain structural and attribute similarities, advanced SSL tasks intertwining with task-specific data—labeled nodes, for example—offered greater gains. This directed the proposal of sophisticated SSL tasks:

• SelfTask-Distance2Labeled: Predicts the distance to labeled nodes per class, embodying a fusion of task-specific supervision with topological contexts.
• ContextLabel and its Variants (EnsembleLabel and CorrectedLabel): These tasks aim to generate neighborhood label distribution vectors, facilitating regular task equivalence by ensuring nodes with similar neighbor label distributions maintain proximity in learned representations.

Experimental Outcomes

The advanced task implementations, especially SelfTask-CorrectedLabel using Label Propagation (LP) and Iterative Classification Algorithm (ICA), surpassed the baseline GCN models and recent SSL benchmarks (like M3S) across multiple datasets. For instance, SelfTask approaches significantly improved classification accuracy in Cora and Citeseer datasets even when labeled data was scarce, underscoring their robustness and effectiveness.

Practical and Theoretical Implications

The integration of task-specific SSL into GNN training frameworks offers promising prospects for numerous applications, particularly when data labeling is limiting. The analysis provides critical hints towards scalable models that can generalize efficiently across complex graph data structures. It also seeds future investigations into more complex task-guided SSL paradigms, perhaps across dynamic or heterogeneously labeled environments.

The detailed exploration in this paper constitutes a significant stride in enhancing GNN functionality via SSL innovations, presenting both immediate applications and foundational pathways for ongoing AI advancements in graph representation learning. There remains vast potential for expanding this work's scope, with anticipated explorations into additional task-specific equivalences or novel SSL techniques in ever-evolving AI research landscapes.