mlsanityData to 14 Oct 2026

Mixture of Experts for Node Classification

arXiv 2412.00418 ↗First posted 30 Nov 2024cs.SI cs.AIcited by 4

On the map: Mixture-of-experts → MoE for time series and graphs

Abstract (arXiv)

Nodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based on distinct node patterns, resulting in unsatisfactory classification performance. In this paper, we reveal that different node predictors are good at handling nodes with specific patterns and only apply one node predictor uniformly could lead to suboptimal result. To mitigate this gap, we propose a mixture of experts framework, MoE-NP, for node classification. Specifically, MoE-NP combines a mixture of node predictors and strategically selects models based on node patterns. Experimental results from a range of real-world datasets demonstrate significant performance improvements from MoE-NP.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts. Builds on 20 of its 38 references that are in the map. First posted on arXiv 30 Nov 2024.

Builds on

20 of 38 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach5 Jun

Other references · 19

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  3. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
  4. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 826
  5. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  6. 2022Mixture-of-Experts with Expert Choice Routingcited by 623
  7. 2019Multi-scale Attributed Node Embeddingcited by 548
  8. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  9. 2017Representation Learning on Graphs: Methods and Applicationscited by 457
  10. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 443
  11. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 311
  12. 2021Is Homophily a Necessity for Graph Neural Networks?cited by 182
7 more
  1. 2022Finding Global Homophily in Graph Neural Networks When Meeting Heterophilycited by 179
  2. 2018Contextual Stochastic Block Modelscited by 134
  3. 2021Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patternscited by 95
  4. 2021Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalizationcited by 70
  5. 2022Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyondcited by 60
  6. 2021A Survey of Machine Learning for Computer Architecture and Systemscited by 53
  7. 2023LSGNN: Towards General Graph Neural Network in Node Classification by Local Similaritycited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Cooperation of Experts: Fusing Heterogeneous Information with Large Margincited by 6
  2. 2025ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inferencecited by 5
  3. 2025Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networkscited by 2
  4. 2026PreGS: A Parameter-Transfer-Based Multi-Expert Graph Neural Network for Node Classificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification
  2. 2025Training Diverse Graph Experts for Ensembles: A Systematic Empirical Study
  3. 2024Mixture of Link Predictors on Graphs
  4. 2023Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
  5. 2025Adaptive Graph Mixture of Residual Experts: Unsupervised Learning on Diverse Graphs with Heterogeneous Specialization
  6. 2024Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
  7. 2025Mixture of Experts (MoE): A Big Data Perspective
  8. 2025Mixture of Raytraced Experts