Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment

Alessandro Bicciato, Luca Cosmo, Giorgia Minello, Luca Rossi, Andrea Torsello

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper we propose a new graph neural network architecture based on the soft-alignment of the graph node features against sets of learned points. In each layer of the network the input node features are transformed by computing their similarity with respect to a set of learned features. The similarity information is then propagated to other nodes in the network, effectively creating a message passing-like mechanism where each node of the graph individually learns what is the optimal message to pass to its neighbours. We perform an ablation study to evaluate the performance of the network under different choices of its hyper-parameters. Finally, we test our model on standard graph-classification benchmarks and we find that it outperforms widely used alternative approaches, including both graph kernels and graph neural networks.

Original languageEnglish
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshops, S+SSPR 2022, Proceedings
EditorsAdam Krzyzak, Ching Y. Suen, Nicola Nobile, Andrea Torsello
PublisherSpringer Science and Business Media Deutschland GmbH
Pages43-53
Number of pages11
ISBN (Print)9783031230271
DOIs
Publication statusPublished - Aug 2022
Externally publishedYes
EventJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022 - Montreal, Canada
Duration: 26 Aug 202227 Aug 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13813 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022
Country/TerritoryCanada
CityMontreal
Period26/08/2227/08/22

Keywords

  • Deep learning
  • Graph neural network

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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