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Related Experiment Video

Updated: Jan 16, 2026

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

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

Published on: June 13, 2025

TGSL: Trade-off graph structure learning via multifaceted graph information bottleneck.

Shuangjie Li¹, Baoming Zhang¹, Jianqing Song¹

¹State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China; Department of Computer Science and Technology, Nanjing University, Nanjing, 210023, China.

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

|September 25, 2025

Summary

This summary is machine-generated.

Trade-off Graph Structure Learning (TGSL) improves graph neural networks (GNNs) by learning optimal graph structures. This method enhances node classification accuracy by minimizing risk and maintaining performance, outperforming existing approaches.

Keywords:

Graph information bottleneck Graph neural networks Graph structure learning Robustness

Related Experiment Videos

Last Updated: Jan 16, 2026

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

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

Published on: June 13, 2025

Area of Science:

Machine Learning
Graph Neural Networks
Data Mining

Background:

Graph neural networks (GNNs) excel at processing graph data for node classification.
Observed graph structures in real-world data are often suboptimal, hindering GNN performance.
Existing GNNs rely on direct message passing over observed structures.

Purpose of the Study:

To address the performance degradation of GNNs caused by suboptimal graph structures.
To propose a novel method, Trade-off Graph Structure Learning (TGSL), for learning effective graph structures.
To enhance node classification accuracy and robustness in GNNs.

Main Methods:

Empirical analysis demonstrating the impact of graph structures on GNN performance.
Development of TGSL, guided by the Graph Information Bottleneck (GIB) principle and Mutual Information (MI).
Integration of global feature and structure augmentation, followed by structure refinement and redefinition.
Optimization using multifaceted GIB to balance empirical risk minimization and information preservation.

Main Results:

TGSL learns minimal sufficient graph structures that minimize empirical risk while preserving essential information.
The method demonstrates superior performance across various datasets under both clean and attacked conditions.
TGSL exhibits significant robustness compared to state-of-the-art GNN baselines.

Conclusions:

TGSL effectively learns optimal graph structures, enhancing GNN performance for node classification.
The proposed method offers a robust solution for handling suboptimal graph structures in real-world applications.
TGSL represents a significant advancement in learning-based graph structure optimization for GNNs.