Learning with Multigraph Convolutional Filters

• URL: http://arxiv.org/abs/2210.16272v1
• Date: Fri, 28 Oct 2022 17:00:50 GMT
• Title: Learning with Multigraph Convolutional Filters
• Authors: Landon Butler, Alejandro Parada-Mayorga, Alejandro Ribeiro
• Abstract summary: We introduce multigraph convolutional neural networks (MGNNs) as stacked and layered structures where information is processed according to an MSP model. We also develop a procedure for tractable computation of filter coefficients in the MGNNs and a low cost method to reduce the dimensionality of the information transferred between layers.
• Score: 153.20329791008095
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a convolutional architecture to perform learning when information is supported on multigraphs. Exploiting algebraic signal processing (ASP), we propose a convolutional signal processing model on multigraphs (MSP). Then, we introduce multigraph convolutional neural networks (MGNNs) as stacked and layered structures where information is processed according to an MSP model. We also develop a procedure for tractable computation of filter coefficients in the MGNN and a low cost method to reduce the dimensionality of the information transferred between layers. We conclude by comparing the performance of MGNNs against other learning architectures on an optimal resource allocation task for multi-channel communication systems.

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