Supervised Graph Contrastive Learning for Few-shot Node Classification

TL;DR

This paper introduces a supervised graph contrastive learning framework that effectively addresses few-shot node classification without relying on meta-learning, demonstrating superior performance on benchmark datasets.

Contribution

The paper proposes a novel supervised contrastive learning framework with data augmentation and multi-scale contrast for few-shot node classification, challenging the reliance on meta-learning.

Findings

Outperforms state-of-the-art meta-learning methods on benchmarks

Effective data augmentation and multi-scale contrast improve performance

Framework is simple and does not require meta-learning procedures

Abstract

Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis. But given the high cost of graph annotation or labeling, we face a severe graph label-scarcity problem, i.e., a graph might have a few labeled nodes. One example of such a problem is the so-called \textit{few-shot node classification}. A predominant approach to this problem resorts to \textit{episodic meta-learning}. In this work, we challenge the status quo by asking a fundamental question whether meta-learning is a must for few-shot node classification tasks. We propose a new and simple framework under the standard few-shot node classification setting as an alternative to meta-learning to learn an effective graph encoder. The framework consists of supervised graph contrastive learning with novel mechanisms for data augmentation, subgraph encoding, and multi-scale contrast on graphs. Extensive experiments on three benchmark datasets (CoraFull, Reddit, Ogbn) show that the new framework significantly outperforms state-of-the-art meta-learning based methods.