Graph-time convolutional neural networks

Elvin Isufi, Gabriele Mazzola

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop a first principle graph-time convolutional neural network (GTCNN). The GTCNN is a compositional architecture with each layer comprising a graph-time convolutional module, a graphtime pooling module, and a nonlinearity. We develop a graph-time convolutional filter by following the shift-and-sum principles of the convolutional operator to learn higher-level features over the product graph. The product graph itself is parametric so that we can learn also the spatiotemporal coupling from data. We develop a zero-pad pooling that preserves the spatial graph (the prior about the data) while reducing the number of active nodes and the parameters. Experimental results with synthetic and real data corroborate the different components and compare with baseline and state-of-the-art solutions.

Original languageEnglish
Title of host publication2021 IEEE Data Science and Learning Workshop (DSLW)
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-6654-2825-5
ISBN (Print)978-1-6654-2826-2
DOIs
Publication statusPublished - 2021
Event2021 IEEE Data Science and Learning Workshop, DSLW 2021 - Toronto, Canada
Duration: 5 Jun 20216 Jun 2021

Conference

Conference2021 IEEE Data Science and Learning Workshop, DSLW 2021
CountryCanada
CityToronto
Period5/06/216/06/21

Bibliographical note

Accepted author manuscript

Keywords

  • Graph neural networks
  • Graph signal processing
  • Graph-time neural networks
  • Spatiotemporal learning

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