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Multiplex Graph Networks for Multimodal Brain Network Analysis

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arxiv 2108.00158 v1 pith:P3B5OW6L submitted 2021-07-31 cs.CV

Zhaoming Kong , Lichao Sun , Hao Peng , Liang Zhan , Yong Chen , Lifang He This is my paper · ORCID
classification cs.CV
keywords multimodalmultiplexnetworkbraingraphanalysismgnetmodel
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In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis. The proposed method integrates tensor representation into the multiplex GCN model to extract the latent structures of a set of multimodal brain networks, which allows an intuitive 'grasping' of the common space for multimodal data. Multimodal representations are then generated with multiplex GCNs to capture specific graph structures. We conduct classification task on two challenging real-world datasets (HIV and Bipolar disorder), and the proposed MGNet demonstrates state-of-the-art performance compared to competitive benchmark methods. Apart from objective evaluations, this study may bear special significance upon network theory to the understanding of human connectome in different modalities. The code is available at https://github.com/ZhaomingKong/MGNets.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

    cs.LG 2026-07 conditional novelty 6.0 of 10

    A federated pretraining-and-prompt-tuning framework that aligns image, text, and graph-topology information across privacy-separated clients claims consistent state-of-the-art results on 12 multimodal graph datasets.

  2. FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

    cs.LG 2026-07 conditional novelty 5.0 of 10

    FedOGL blends replay, distillation, structure-basis gradient shielding, and prototype consolidation to cut catastrophic forgetting in federated open-world multimodal graph learning, reporting 42.67% less forgetting th...

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