arxiv_ml 95% Match Research Paper Researchers in graph machine learning,Machine learning engineers,Data scientists 2 weeks ago

Training Diverse Graph Experts for Ensembles: A Systematic Empirical Study

graph-neural-networks › graph-learning

📄 Abstract

Abstract: Graph Neural Networks (GNNs) have become essential tools for learning on relational data, yet the performance of a single GNN is often limited by the heterogeneity present in real-world graphs. Recent advances in Mixture-of-Experts (MoE) frameworks demonstrate that assembling multiple, explicitly diverse GNNs with distinct generalization patterns can significantly improve performance. In this work, we present the first systematic empirical study of expert-level diversification techniques for GNN ensembles. Evaluating 20 diversification strategies -- including random re-initialization, hyperparameter tuning, architectural variation, directionality modeling, and training data partitioning -- across 14 node classification benchmarks, we construct and analyze over 200 ensemble variants. Our comprehensive evaluation examines each technique in terms of expert diversity, complementarity, and ensemble performance. We also uncovers mechanistic insights into training maximally diverse experts. These findings provide actionable guidance for expert training and the design of effective MoE frameworks on graph data. Our code is available at https://github.com/Hydrapse/bench-gnn-diversification.

Authors (7)

Gangda Deng

Yuxin Yang

Ömer Faruk Akgül

Hanqing Zeng

Yinglong Xia

Rajgopal Kannan

+1 more

Submitted

October 21, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper presents the first systematic empirical study of expert-level diversification techniques for GNN ensembles. It evaluates 20 strategies across 14 benchmarks, analyzing over 200 ensemble variants to understand how diversity impacts performance. The work provides mechanistic insights into training maximally diverse experts, aiming to improve GNN generalization by assembling explicitly diverse models.

Business Value

Provides a systematic guide for practitioners on how to build more robust and accurate graph-based models by leveraging ensemble techniques. This can lead to improved performance in applications like recommendation systems, social network analysis, and drug discovery.

Paper Metadata

Innovation Type

Empirical Study / Methodology

Deployment Feasibility

Moderate. Ensemble methods can increase computational cost and complexity compared to single models, but the study aims to identify effective strategies.

Limitations Addressed

Addresses the performance limitations of single GNNs due to graph heterogeneity. Explores how to effectively combine multiple GNNs (experts) to achieve better generalization and performance.

Performance Gains

Implied significant gains through ensemble methods, though specific numbers are not detailed in the abstract.

Technical Tags

graph neural networksensemblesmixture-of-expertsdiversification strategiesnode classificationempirical studyhyperparameter tuningarchitectural variationdata partitioning

Research Topics

Graph Neural Network EnsemblesMixture-of-ExpertsModel DiversificationEmpirical EvaluationNode Classification Benchmarks

Methods & Architectures

Systematic empirical studyEvaluation of 20 diversification strategiesEnsemble constructionAnalysis of expert diversity, complementarity, and performance Graph Neural Networks (GNNs)Mixture-of-Experts (MoE) frameworksEnsemble models

Applications & Tasks

Machine learning on graphs Relational data analysis Improving GNN performanceHandling graph heterogeneity Node classification

Datasets & Benchmarks

Benchmarks

14 node classification benchmarks

Expert diversityComplementarityEnsemble performance

Related Fields

Machine LearningGraph TheoryEnsemble MethodsArtificial Intelligence

Keywords

graph neural networksensemblesmixture-of-expertsmodel diversificationnode classificationempirical studyheterogeneityrelational datageneralizationcomplementarityexpert diversityhyperparameter tuningarchitectural variationdata partitioning

Academic Context

#Graph Neural Network Ensembles#Mixture-of-Experts#Model Diversification#Empirical Evaluation#Node Classification Benchmarks

Commercial Potential

Potential Products

Automated GNN ensemble construction toolsLibraries for GNN diversification strategies

Target Industries

Social MediaE-commerceFinanceBiotechnologyTelecommunications

Use Case Examples

Building a more accurate social network analysis tool by combining multiple specialized GNNs.Developing a robust drug discovery platform that leverages diverse GNNs for molecular property prediction.

Competitive Edge

Positions itself as a foundational empirical study that clarifies best practices for GNN ensembles, aiming to guide future research and development beyond ad-hoc ensemble methods.

Market Opportunity

Large and growing market for graph analytics and AI solutions.

Revenue Models

Integration into ML platformsconsulting services for optimizing GNN ensembles.

Resource Requirements

Compute Needs

Likely requires significant computational resources for training and evaluating numerous ensemble variants across multiple benchmarks.

Data Requirements

Requires access to various graph datasets suitable for node classification tasks.

Deployment Constraints

Ensemble models can be more complex to deploy and manage than single models. The benefits must outweigh the added complexity.

Scalability

Scalability depends on the chosen GNN architectures and the number of experts in the ensemble. MoE architectures are designed with scalability in mind.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for practical adoption and integration into ML platforms.

Patent Potential

Low, as it's primarily an empirical study of existing techniques.

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