Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning
arxiv(2023)
摘要
Self-Supervised Learning (SSL) has shown significant potential and has
garnered increasing interest in graph learning. However, particularly for
generative SSL methods, its potential in Heterogeneous Graph Learning (HGL)
remains relatively underexplored. Generative SSL utilizes an encoder to map the
input graph into a latent representation and a decoder to recover the input
graph from the latent representation. Previous HGL SSL methods generally design
complex strategies to capture graph heterogeneity, which heavily rely on
contrastive view construction strategies that are often non-trivial. Yet,
refining the latent representation in generative SSL can effectively improve
graph learning results. In this study, we propose HGVAE, a generative SSL
method specially designed for HGL. Instead of focusing on designing complex
strategies to capture heterogeneity, HGVAE centers on refining the latent
representation. Specifically, HGVAE innovatively develops a contrastive task
based on the latent representation. To ensure the hardness of negative samples,
we develop a progressive negative sample generation (PNSG) mechanism that
leverages the ability of Variational Inference (VI) to generate high-quality
negative samples. As a pioneer in applying generative SSL for HGL, HGVAE
refines the latent representation, thereby compelling the model to learn
high-quality representations. Compared with various state-of-the-art (SOTA)
baselines, HGVAE achieves impressive results, thus validating its superiority.
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