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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Enhancing signed graph neural networks through curriculum-based training.

Zeyu Zhang¹, Lu Li¹, Xingyu Ji¹

¹National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, China.

Neural Networks : the Official Journal of the International Neural Network Society

|August 20, 2025

Summary

This study introduces a novel curriculum learning framework for Signed Graph Neural Networks (SGNNs), improving model accuracy and stability by training on edges ordered by difficulty. The CSG framework enhances SGNN performance on real-world signed graph data.

Keywords:

Curriculum learning Graph neural networks Signed graph representation learning

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Area of Science:

Graph Theory
Machine Learning
Network Science

Background:

Signed graphs model complex relationships with positive and negative connections.
Signed Graph Neural Networks (SGNNs) are emerging tools for analyzing signed graphs.
Current SGNN training lacks a structured approach, using random sample ordering.

Purpose of the Study:

To develop a specialized training plan for SGNNs.
To address the challenge of varying edge learning difficulties in signed graphs.
To improve the performance and stability of SGNN models.

Main Methods:

Proposed a Curriculum representation learning framework for Signed Graphs (CSG).
Developed a lightweight difficulty measurer for edges in signed graphs.
Implemented a scheduler to order training samples from easy to difficult for SGNNs.

Main Results:

Enhanced accuracy of popular SGNN models by up to 23.7%.
Reduced standard deviation by 8.4%, improving model stability.
Empirically validated on six real-world signed graph datasets.

Conclusions:

Curriculum learning significantly benefits SGNNs by optimizing training sample order.
The CSG framework offers a practical and effective method for training SGNNs.
The proposed approach leads to more accurate and stable signed graph representation learning.