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Cited by 341

The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index.

Graph Neural Networks in Network Neuroscience

Open access
Bessadok, Alaa, Mahjoub, Mohamed Ali, Rekik, Islem·2023·IEEE Transactions on Pattern Analysis and Machine Intelligence

Abstract

Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.

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Metadata

Authors
Bessadok, A., Mahjoub, M. A., & Rekik, I.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2023-05-01
Volume
45
Issue
5
Pages
5833-5848
ISSN
  • 0162-8828 (ISSN-L)
  • 2160-9292
  • 1939-3539
OpenAlex
W3169752883
Cited by
341
References
110
FWCI
31.64
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Graph Neural Networks in Network Neuroscience. https://app.cochat.ai/papers/10.1109/tpami.2022.3209686
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Bessadok, A., Mahjoub, M. A., & Rekik, I. (2023). Graph Neural Networks in Network Neuroscience. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2022.3209686

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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