Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications

Kamilia Zaripova*, Luca Cosmo, Anees Kazi, Seyed Ahmad Ahmadi, Michael M. Bronstein, Nassir Navab

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works have shown that considering relationships between input data samples has a positive regularizing effect on the downstream task in healthcare applications. These relationships are naturally modeled by a (possibly unknown) graph structure between input samples. In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation. We assume an initially unknown latent-graph structure between graph-valued input data and propose to learn a parametric model for message passing within and across input graph samples, end-to-end along with the latent structure connecting the input graphs. Further, we introduce a Node Degree Distribution Loss (NDDL) that regularizes the predicted latent relationships structure. This regularization can significantly improve the downstream task. Moreover, the obtained latent graph can represent patient population models or networks of molecule clusters, providing a level of interpretability and knowledge discovery in the input domain, which is of particular value in healthcare.

Original languageEnglish
Article number102839
JournalMedical Image Analysis
Volume88
DOIs
StatePublished - Aug 2023
Externally publishedYes

Funding

FundersFunder number
DAIS departement of Ca’ Foscari University of Venice
TUM-ICL
Bundesministerium für Bildung und ForschungFKZ 13GW0469C

    Keywords

    • Graph deep learning
    • Knowledge discovery

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