mlsanityData to 14 Oct 2026

MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations

arXiv 2104.03895 ↗First posted 4 Apr 2021cs.SI cs.LG q-bio.QMcited by 8

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Abstract (arXiv)

With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net; https://github.com/basiralab/MGN-Net), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer's disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 18 of its 79 references that are in the map. First posted on arXiv 4 Apr 2021.

Builds on

18 of 79 references.

References · 18

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6 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 1.

8 papers.

  1. 2022Deep reinforcement learning guided graph neural networks for brain network analysiscited by 12
  2. 2021Heterogeneous Graph-Based Multimodal Brain Network Learningcited by 5
  3. 2022Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applicationscited by 4
  4. 2022Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mappingcited by 4
  5. 2022Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classificationcited by 1
  6. 2023Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discoverycited by 0
  7. 2023Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classificationcited by 0
  8. 2024Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarioscited by 0