Multigraph classification using learnable integration network with application to gender fingerprinting

Nada Chaari, Mohammed Amine Gharsallaoui, Hatice Camgöz Akdağ, Islem Rekik*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Multigraphs with heterogeneous views present one of the most challenging obstacles to classification tasks due to their complexity. Several works based on feature selection have been recently proposed to disentangle the problem of multigraph heterogeneity. However, such techniques have major drawbacks. First, the bulk of such works lies in the vectorization and the flattening operations, failing to preserve and exploit the rich topological properties of the multigraph. Second, they learn the classification process in a dichotomized manner where the cascaded learning steps are pieced in together independently. Hence, such architectures are inherently agnostic to the cumulative estimation error from step to step. To overcome these drawbacks, we introduce MICNet (multigraph integration and classifier network), the first end-to-end graph neural network based model for multigraph classification. First, we learn a single-view graph representation of a heterogeneous multigraph using a GNN based integration model. The integration process in our model helps tease apart the heterogeneity across the different views of the multigraph by generating a subject-specific graph template while preserving its geometrical and topological properties conserving the node-wise information while reducing the size of the graph (i.e., number of views). Second, we classify each integrated template using a geometric deep learning block which enables us to grasp the salient graph features. We train, in end-to-end fashion, these two blocks using a single objective function to optimize the classification performance. We evaluate our MICNet in gender classification using brain multigraphs derived from different cortical measures. We demonstrate that our MICNet significantly outperformed its variants thereby showing its great potential in multigraph classification.

Original languageEnglish
Pages (from-to)250-263
Number of pages14
JournalNeural Networks
Volume151
DOIs
Publication statusPublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Funding

This work was funded by generous grants from the European H2020 Marie Sklodowska-Curie action (Grant No. 101003403, http://basira-lab.com/normnets/ ) to I.R. and the Scientific and Technological Research Council of Turkey to I.R. under the TUBITAK 2232 Fellowship for Outstanding Researchers (no. 118C288 , http://basira-lab.com/reprime/ ). However, all scientific contributions made in this project are owned and approved solely by the authors. N.C. is supported by the TUBITAK 2232 Fellowship . This work was funded by generous grants from the European H2020 Marie Sklodowska-Curie action (Grant No. 101003403,http://basira-lab.com/normnets/) to I.R. and the Scientific and Technological Research Council of Turkey to I.R. under the TUBITAK 2232Fellowship for Outstanding Researchers (no. 118C288, http://basira-lab.com/reprime/). However, all scientific contributions made in this project are owned and approved solely by the authors. N.C. is supported by the TUBITAK 2232 Fellowship.

FundersFunder number
European H2020 Marie Sklodowska-Curie action101003403
TUBITAK
TUBITAK 2232 Fellowship for Outstanding Researchers118C288
TUBITAK 2232Fellowship for Outstanding Researchers
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

    Keywords

    • Gender differences
    • Geometric deep learning (GDL)
    • Graph neural network (GNN)
    • Multigraph classification
    • Multigraph integration

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