Random Geometric Graph Alignment with Graph Neural Networks

• URL: http://arxiv.org/abs/2402.07340v1
• Date: Mon, 12 Feb 2024 00:18:25 GMT
• Title: Random Geometric Graph Alignment with Graph Neural Networks
• Authors: Suqi Liu and Morgane Austern
• Abstract summary: We show that a graph neural network can recover an unknown one-to-one mapping between the vertices of two graphs. We also prove that our conditions on the noise level are tight up to logarithmic factors. We demonstrate that when the noise level is at least constant this direct matching fails to have perfect recovery while the graph neural network can tolerate noise level growing as fast as a power of the size of the graph.
• Score: 8.08963638000146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We characterize the performance of graph neural networks for graph alignment problems in the presence of vertex feature information. More specifically, given two graphs that are independent perturbations of a single random geometric graph with noisy sparse features, the task is to recover an unknown one-to-one mapping between the vertices of the two graphs. We show under certain conditions on the sparsity and noise level of the feature vectors, a carefully designed one-layer graph neural network can with high probability recover the correct alignment between the vertices with the help of the graph structure. We also prove that our conditions on the noise level are tight up to logarithmic factors. Finally we compare the performance of the graph neural network to directly solving an assignment problem on the noisy vertex features. We demonstrate that when the noise level is at least constant this direct matching fails to have perfect recovery while the graph neural network can tolerate noise level growing as fast as a power of the size of the graph.

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