Multi-Semantic Aware Self-Supervised Learning for Multi-Label Node Classification

Abstract

Graph self-supervised learning aims to mine intrinsic signals from graph data itself to train models. It enables the acquisition of high-quality representations without manual annotations, making it suitable for various label-scarce scenarios and thus garnering substantial interest. Existing graph self-supervised methods typically assume that each node possesses a single semantic meaning. However, many real-world entities exhibit multiple semantics, where a single node may simultaneously belong to multiple categories. Since existing approaches are mainly designed for single-semantic settings, they often struggle to accurately capture the multiple semantics within nodes. Meanwhile, existing multi-label graph learning methods mainly depend on extensive manual annotations, which are costly and of limited applicability. To address this, we make a bold attempt to extend graph self-supervised learning to multi-label graphs. It is particularly challenging, as lacking labels makes it difficult to identify multiple semantics, let alone determine the number of underlying categories for nodes. To this end, we propose a Multi-semantic Aware Self-Supervised pretraining method (MASS) for multi-label graphs. Specifically, we propose a multi-pseudo-label decomposition mechanism, enabling adaptive learning of multiple semantics within nodes without annotations. Extensive results validates the effectiveness of MASS, and it achieved competitive performances with supervised baselines.