mlsanityData to 7 Oct 2026

Graph Classification by Mixture of Diverse Experts

arXiv 2103.15622 ↗First posted 29 Mar 2021cs.LGcited by 16

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Abstract (arXiv)

Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imbalanced. Recently, Graph Neural Network (GNN) models have achieved superior performance on various real-world datasets. Despite their success, most of current GNN models largely overlook the important setting of imbalanced class distribution, which typically results in prediction bias towards majority classes. To alleviate the prediction bias, we propose to leverage semantic structure of dataset based on the distribution of node embedding. Specifically, we present GraphDIVE, a general framework leveraging mixture of diverse experts (i.e., graph classifiers) for imbalanced graph classification. With a divide-and-conquer principle, GraphDIVE employs a gating network to partition an imbalanced graph dataset into several subsets. Then each expert network is trained based on its corresponding subset. Experiments on real-world imbalanced graph datasets demonstrate the effectiveness of GraphDIVE.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 42 of its 67 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

42 of 67 references.

References · 42

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30 more
  1. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 3; 2025: 5; 2026: 4.

16 papers.

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  1. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  2. 2021Think about it! Improving defeasible reasoning by first modeling the question scenariocited by 20
  3. 2023Learning Topology-Specific Experts for Molecular Property Predictioncited by 15
  4. 2024One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphscited by 12
  5. 2024Hierarchical Mixture of Experts: Generalizable Learning for High-Level Synthesiscited by 9
  6. 2024KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Datacited by 4
  7. 2025ViTE: Virtual Graph Trajectory Expert Router for Pedestrian Trajectory Predictioncited by 4
  8. 2025Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibrationcited by 3
  9. 2025Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learningcited by 2
  10. 2025Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networkscited by 2
  11. 2026Modality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learningcited by 1
  12. 2026Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learningcited by 1
4 more
  1. 2023Enhancing Mobile Face Anti-Spoofing: A Robust Framework for Diverse Attack Types under Screen Flashcited by 0
  2. 2025Adaptive Graph Mixture of Residual Experts: Unsupervised Learning on Diverse Graphs with Heterogeneous Specializationcited by 0
  3. 2026Zero-shot Generalizable Graph Anomaly Detection with Mixture of Riemannian Expertscited by 0
  4. 2026Evolutionary Router Feature Generation for Zero-Shot Graph Anomaly Detection with Mixture-of-Expertscited by 0