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Self-Supervised Graph Representation Learning via Global Context Prediction
Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu, Qinghua Zheng
TL;DR
Graph representation learning needs objectives that exploit abundant unlabeled data without handcrafted annotation. S2GRL uses hop count between node pairs as free supervision for predicting relative contextual position and learning global-context-aware representations. Across node classification, clustering, and link prediction, it is competitive with state-of-the-art unsupervised methods and sometimes exceeds supervised baselines.
Problem
Choosing an objective that learns desirable node representations without handcrafted annotation remains challenging, while the effectiveness of self-supervision in graph learning is unclear.
Method
S2GRL trains a neural network to predict the hop-count-based relative contextual position between pairs of nodes, using global graph context as free supervision.
Results
S2GRL is competitive with state-of-the-art unsupervised methods across three learning tasks and sometimes outperforms strong supervised baselines.
Takeaways & Limitations
Hop count is presented as a useful natural supervisory signal for learning global-context-aware node representations from unlabeled graphs.
Takeaways & Limitations
The global-context formulation assumes all other nodes constitute a node’s context and relies on an independence assumption in factorizing the objective.
Abstract
from arXiv · showhide
To take full advantage of fast-growing unlabeled networked data, this paper introduces a novel self-supervised strategy for graph representation learning by exploiting natural supervision provided by the data itself. Inspired by human social behavior, we assume that the global context of each node is composed of all nodes in the graph since two arbitrary entities in a connected network could interact with each other via paths of varying length. Based on this, we investigate whether the global context can be a source of free and effective supervisory signals for learning useful node representations. Specifically, we randomly select pairs of nodes in a graph and train a well-designed neural net to predict the contextual position of one node relative to the other. Our underlying hypothesis is that the representations learned from such within-graph context would capture the global topology of the graph and finely characterize the similarity and differentiation between nodes, which is conducive to various downstream learning tasks. Extensive benchmark experiments including node classification, clustering, and link prediction demonstrate that our approach outperforms many state-of-the-art unsupervised methods and sometimes even exceeds the performance of supervised counterparts.
1 Introduction
The paper addresses how to learn useful node representations without handcrafted labels by using graph-structured data itself as supervision. S2GRL predicts relative contextual position between node pairs, using hop count to capture global topology, and shows competitive or stronger benchmark performance.
- 1 Introduction: Unsupervised graph learning avoids expensive manual annotation, but choosing an objective that learns desirable node representations remains challenging.The motivation is to exploit abundant unlabeled networked data for downstream graph tasks.
- 1 Introduction: Hop count provides free supervisory information because all nodes can form a global context through paths of varying lengths.Shorter paths indicate greater interaction, while the resulting signal is obtained directly from unlabeled graph data.
- 1 Introduction: GCNs can capture higher-order relations by stacking layers, but increasing depth causes over-smoothing and makes the appropriate layer count difficult to choose.The paper motivates hop-count supervision as an alternative way to capture global structure.
- 1 Introduction: S2GRL predicts the relative contextual position of one node to another, requiring representations that encode global topology while distinguishing node similarity and dissimilarity.The task uses pairs of nodes and asks whether one is one hop, two hops, or more away from the other.
- 1 Introduction: Across node classification, clustering, and link prediction experiments, S2GRL is competitive with state-of-the-art unsupervised methods and sometimes surpasses strong supervised baselines.The reported experiments evaluate the framework on three common learning tasks.
2 Related Work
Related work frames self-supervision as learning from data-generated pretext signals and examines its emerging application to graph representation learning. Existing graph methods include factorization, random-walk, and neural approaches, while deeper GCNs face over-smoothing.
- 2 Related Work: Self-supervised learning trains a pretext task using supervisory signals automatically generated from the data itself.Prior applications include image and video tasks such as rotation prediction, temporal ordering, and visual feature learning.
- 2 Related Work: An unsupervised graph-convolution baseline initially improves as layers increase, then degrades when additional layers cause over-smoothing.Figure 2 reports this pattern separately for node classification and clustering.
- 2 Related Work: Whether self-supervision works effectively for graph-structured data remains unclear, motivating this paper’s investigation.Graph representation methods are broadly categorized as factorization-based, random-walk-based, and neural-network-based.
- 2 Related Work: Most GCN-based methods require annotated labels, whereas unsupervised algorithms avoid external labels but face a different set of performance considerations.The passage contrasts supervised graph convolutional methods with unsupervised alternatives.
3 Methodology
S2GRL learns node representations by using graph-derived hop counts as pseudo-labels for predicting relative contextual positions between node pairs. Its hop-based global context captures topology while preserving fine-grained interaction differences for downstream tasks.
- 3 Methodology: Each node’s global context includes nodes reachable through shortest paths of different lengths, rather than only a limited local or random-walk window.This assumption treats all nodes in the graph as potential context and uses path length to distinguish their interaction strength.
- 3 Methodology: The formulation avoids a conventional |V|-class context classifier because every node is positive under the global-context assumption and such a classifier is computationally costly.Instead, S2GRL predicts contextual position using hop-based supervision.
- 3 Methodology: A hop-based global context partitions nodes into disjoint k-hop contexts according to shortest-path distance from the target node.For node vi, C_i contains nodes vj with d_ij = k, where k ranges from 1 through the node-specific upper bound δ_i.
- 3 Methodology: S2GRL trains a neural network to predict the relative contextual position between pairs of nodes, using hop count as natural supervision.The objective assigns pseudo-labels according to each node pair’s contextual position and learns representations through this prediction task.
- 3 Methodology: Hop counts provide fine-grained pseudo-labels that encourage representations to encode global topology and distinguish node similarity and dissimilarity.The resulting representations are intended to support downstream classification and clustering.
- 3 Methodology: The method groups higher-hop contexts into broader major categories when their distinctions are vague, while retaining separate categories for clearly different interaction ranges.One-hop and two-hop contexts are treated as distinct, whereas higher-hop contexts may be merged; α denotes the number of predefined classes.
4 Experiments
Experiments evaluate S2GRL across transductive and inductive classification, clustering, and link prediction using standard datasets and unsupervised baselines. Results show strong performance across tasks, while label-category design affects representation quality.
- Experimental setup: Experiments use citation, protein-interaction, and social-network datasets across three tasks, with graph-convolutional encoders and class-balanced node-pair sampling.The sampling strategy addresses computational cost, memory use, and imbalance among hop-based classes.
- Node classification: S2GRL outperforms all other unsupervised algorithms on node classification, especially on Pubmed and PPI, and can surpass supervised models such as GCN and GWNN.The reported results identify S2GRL as particularly effective for node classification and state that it achieves the best result on Pubmed.
- Link prediction: S2GRL consistently outperforms DGI and node2vec on link prediction under different edge removal rates.The comparison uses AUC scores for link prediction.
- Clustering: S2GRL achieves competitive clustering performance, including the highest NMI on Pubmed, although DGI performs best on Cora and Citeseer.The paper attributes the advantage over AGC to modeling global topology and fine-grained similarity rather than only high-order relations.
- Further discussion on label categories: Separating 1-hop, 2-hop, and 3-hop contexts improves representations, whereas distinguishing 4-hop and higher-hop contexts further degrades performance.The adopted scheme combines 3-hop and 4-hop contexts into one class because higher-hop distinctions are less clear.
5 Conclusion
The paper presents S2GRL as a novel self-supervised framework for learning node representations from free supervisory signals in graph-structured data. Extensive experiments demonstrate its effectiveness and motivate further research in self-supervised graph representation learning.
- Conclusion: S2GRL explores free supervisory signals in graph-structured data for node representation learning as a novel self-supervised framework.The paper characterizes this as the first attempt, to its knowledge, to investigate such signals in graph representation learning.
- Conclusion: Extensive experiments demonstrate the effectiveness of S2GRL and support further research in self-supervised graph representation learning.