SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks

Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this problem is to infer a task-specific latent structure and then apply a GNN to the inferred graph. Unfortunately, the space...

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Bibliographic Details
Published in:arXiv.org 2021-10
Main Authors: Fatemi, Bahare, Layla El Asri, Seyed Mehran Kazemi
Format: Article
Language:English
Subjects:
Graph neural networks
Learning
Neural networks
Nodes
Parameters
Supervision
Online Access:Get full text
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Summary:Graph neural networks (GNNs) work well when the graph structure is provided. However, this structure may not always be available in real-world applications. One solution to this problem is to infer a task-specific latent structure and then apply a GNN to the inferred graph. Unfortunately, the space of possible graph structures grows super-exponentially with the number of nodes and so the task-specific supervision may be insufficient for learning both the structure and the GNN parameters. In this work, we propose the Simultaneous Learning of Adjacency and GNN Parameters with Self-supervision, or SLAPS, a method that provides more supervision for inferring a graph structure through self-supervision. A comprehensive experimental study demonstrates that SLAPS scales to large graphs with hundreds of thousands of nodes and outperforms several models that have been proposed to learn a task-specific graph structure on established benchmarks.
ISSN:2331-8422