Self-supervised Masked Graph Autoencoder via Structure-aware Curriculum
Keywords: Curriculum Learning, Self-supervised Learning, Graph Neural Network
TL;DR: We propose a novel curriculum self-supervised masked graph autoencoder for learning informative node representations.
Abstract: Self-supervised learning (SSL) on graph-structured data has attracted considerable attention recently. Masked graph autoencoder, as one promising generative graph SSL approach that aims to recover masked parts of the input graph data, has shown great success on various downstream graph tasks. However, existing masked graph autoencoders fail to consider the degrees of difficulties of recovering the masked edges that often have different impacts on the model performance, resulting in suboptimal node representations. To tackle this challenge, in this paper, we propose a novel curriculum based self-supervised masked graph autoencoder that is able to capture and leverage the underlying degree of difficulties of data dependencies hidden in edges, and design better mask-reconstruction pretext tasks for learning informative node representations. Specifically, we first design a difficulty measurer to identify the underlying structural degree of difficulties of edges during the masking step. Then, we adopt a self-paced scheduler to determine the order of masking edges, which encourages the graph encoder to learn from easy parts to difficult parts. Finally, the masked edges are gradually incorporated into the reconstruction pretext task, leading to high-quality node representations. Experiments on several real-world node classification and link prediction datasets demonstrate the superiority of our proposed method over state-of-the-art graph self-supervised learning baselines. This work is the first study of curriculum strategy for masked graph autoencoders, to the best of our knowledge.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 13431
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