Computer Science > Machine Learning
[Submitted on 27 Sep 2023 (v1), last revised 23 Jun 2024 (this version, v2)]
Title:Graph-level Representation Learning with Joint-Embedding Predictive Architectures
View PDF HTML (experimental)Abstract:Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-based model by predicting the latent representation of a target signal y from the latent representation of a context signal x. JEPAs bypass the need for negative and positive samples, traditionally required by contrastive learning while avoiding the overfitting issues associated with generative pretraining. In this paper, we show that graph-level representations can be effectively modeled using this paradigm by proposing a Graph Joint-Embedding Predictive Architecture (Graph-JEPA). In particular, we employ masked modeling and focus on predicting the latent representations of masked subgraphs starting from the latent representation of a context subgraph. To endow the representations with the implicit hierarchy that is often present in graph-level concepts, we devise an alternative prediction objective that consists of predicting the coordinates of the encoded subgraphs on the unit hyperbola in the 2D plane. Through multiple experimental evaluations, we show that Graph-JEPA can learn highly semantic and expressive representations, as shown by the downstream performance in graph classification, regression, and distinguishing non-isomorphic graphs. The code will be made available upon acceptance.
Submission history
From: Geri Skenderi [view email][v1] Wed, 27 Sep 2023 20:42:02 UTC (618 KB)
[v2] Sun, 23 Jun 2024 09:31:04 UTC (803 KB)
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