Pooling Strategies for Simplicial Convolutional Networks

• URL: http://arxiv.org/abs/2210.05490v1
• Date: Tue, 11 Oct 2022 14:45:51 GMT
• Title: Pooling Strategies for Simplicial Convolutional Networks
• Authors: Domenico Mattia Cinque, Claudio Battiloro, Paolo Di Lorenzo
• Abstract summary: The goal of this paper is to introduce pooling strategies for simplicial convolutional neural networks. Inspired by graph pooling methods, we introduce a general formulation for a simplicial pooling layer. The general layer is then customized to design four different pooling strategies.
• Score: 18.80397868603073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of this paper is to introduce pooling strategies for simplicial convolutional neural networks. Inspired by graph pooling methods, we introduce a general formulation for a simplicial pooling layer that performs: i) local aggregation of simplicial signals; ii) principled selection of sampling sets; iii) downsampling and simplicial topology adaptation. The general layer is then customized to design four different pooling strategies (i.e., max, top-k, self-attention, and separated top-k) grounded in the theory of topological signal processing. Also, we leverage the proposed layers in a hierarchical architecture that reduce complexity while representing data at different resolutions. Numerical results on real data benchmarks (i.e., flow and graph classification) illustrate the advantage of the proposed methods with respect to the state of the art.

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