mlsanityData to 7 Oct 2026

Multiplex Graph Networks for Multimodal Brain Network Analysis

arXiv 2108.00158 ↗First posted 31 Jul 2021cs.CVcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 31 Jul 2021.

Builds on

15 of 42 references.

References · 15

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  5. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  6. 2020Tensor Graph Convolutional Networks for Text Classificationcited by 49
  7. 2019Graph Neural Network for Interpreting Task-fMRI Biomarkerscited by 40
  8. 2020Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networkscited by 26
  9. 2018Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivitycited by 21
  10. 2018Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Diseasecited by 19
  11. 2018Multi-View Multi-Graph Embedding for Brain Network Clustering Analysiscited by 14
  12. 2018Boosted Sparse and Low-Rank Tensor Regressioncited by 9
3 more
  1. 2020Tensor Graph Convolutional Networks for Multi-relational and Robust Learningcited by 9
  2. 2020Tensor denoising and completion based on ordinal observationscited by 8
  3. 2016Low-rank Multi-view Clustering in Third-Order Tensor Spacecited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 5.

7 papers.

  1. 2026MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learningcited by 11
  2. 2026OpenMAG: A Comprehensive Benchmark for Multimodal-Attributed Graphcited by 11
  3. 2022Modern Views of Machine Learning for Precision Psychiatrycited by 5
  4. 2026Context-aware Modality-Topology Co-Alignment for Multimodal Attributed Graphscited by 1
  5. 2026FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learningcited by 1
  6. 2026Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Frameworkcited by 1
  7. 2025Multimodal Graph Neural Networks for Prognostic Modeling of Brain Network Reorganizationcited by 0