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Dynamic Local Filters in Graph Convolutional Neural Networks

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Image Analysis and Processing – ICIAP 2023 (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14234))

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Abstract

Over the last few years, we have seen increasing data generated from non-Euclidean domains, usually represented as graphs with complex relationships. Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data. In particular, there is a strong interest in performing convolution on graphs using specific GNN architectures, generally called Graph Convolutional Neural Networks (GCNN). This paper presents a novel method to adapt the behaviour of a GCNN using an input-based dynamically generated filter. Notice that the idea of adapting the network behaviour to the inputs they process to maximize the total performances has aroused much interest in the neural networks literature over the years. The experimental assessment confirms the capabilities of the proposed approach, achieving promising results using simple architectures with a low number of filters.

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Notes

  1. 1.

    https://www.kaggle.com/c/MerckActivity.

  2. 2.

    https://github.com/mdeff/cnn_graph.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy

    Andrea Apicella, Francesco Isgrò, Andrea Pollastro & Roberto Prevete

  2. Laboratory of Augmented Reality for Health Monitoring (ARHeMLab), Naples, Italy

    Andrea Apicella, Francesco Isgrò, Andrea Pollastro & Roberto Prevete

  3. Laboratory of Artificial Intelligence, Privacy & Applications (AIPA Lab), Naples, Italy

    Andrea Apicella, Francesco Isgrò & Roberto Prevete

  4. Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

    Andrea Pollastro

Authors
  1. Andrea Apicella
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  2. Francesco Isgrò
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  3. Andrea Pollastro
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  4. Roberto Prevete
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Corresponding author

Correspondence to Andrea Apicella .

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Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

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Apicella, A., Isgrò, F., Pollastro, A., Prevete, R. (2023). Dynamic Local Filters in Graph Convolutional Neural Networks. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14234. Springer, Cham. https://doi.org/10.1007/978-3-031-43153-1_34

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  • DOI: https://doi.org/10.1007/978-3-031-43153-1_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43152-4

  • Online ISBN: 978-3-031-43153-1

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