Self-Supervised Graph Representation Learning via Global Context Prediction

TL;DR

This paper proposes a self-supervised graph representation learning method that predicts the global context of nodes, leveraging the entire graph structure to learn useful node embeddings without labels.

Contribution

It introduces a novel global context prediction strategy inspired by social behavior, effectively capturing the graph's topology for improved node representations.

Findings

Outperforms many state-of-the-art unsupervised methods

Sometimes exceeds supervised methods in performance

Effective for node classification, clustering, and link prediction

Abstract

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.