Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks

2025-07-02

AI Summary

Existing conformal prediction methods assume exchangeability, rendering them ill-suited for temporal graphs exhibiting non-exchangeable characteristics—such as evolving graph topology, dynamic node attributes, and temporal label dependencies. This work pioneers the extension of calibration-preserving uncertainty quantification to temporal graph neural networks. We propose a diffusion-based non-homogeneous scoring function that jointly models topological and temporal uncertainties. To enhance adaptability, we design dynamic neighborhood aggregation and adaptive score normalization, and introduce an efficiency-aware optimization algorithm to reduce coverage deviation. Extensive experiments on real-world temporal graphs—including WIKI, REDDIT, DBLP, and an anti-money laundering dataset—demonstrate that our method achieves statistically valid coverage while reducing average prediction set size by 31% compared to state-of-the-art approaches.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Graphical Models
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Conformal prediction for graph neural networks (GNNs) offers a promising framework for quantifying uncertainty, enhancing GNN reliability in high-stakes applications. However, existing methods predominantly focus on static graphs, neglecting the evolving nature of real-world graphs. Temporal dependencies in graph structure, node attributes, and ground truth labels violate the fundamental exchangeability assumption of standard conformal prediction methods, limiting their applicability. To address these challenges, in this paper, we introduce NCPNET, a novel end-to-end conformal prediction framework tailored for temporal graphs. Our approach extends conformal prediction to dynamic settings, mitigating statistical coverage violations induced by temporal dependencies. To achieve this, we propose a diffusion-based non-conformity score that captures both topological and temporal uncertainties within evolving networks. Additionally, we develop an efficiency-aware optimization algorithm that improves the conformal prediction process, enhancing computational efficiency and reducing coverage violations. Extensive experiments on diverse real-world temporal graphs, including WIKI, REDDIT, DBLP, and IBM Anti-Money Laundering dataset, demonstrate NCPNET's capability to ensure guaranteed coverage in temporal graphs, achieving up to a 31% reduction in prediction set size on the WIKI dataset, significantly improving efficiency compared to state-of-the-art methods. Our data and code are available at https://github.com/ODYSSEYWT/NCPNET.

Problem

• Extends conformal prediction to dynamic temporal graphs
• Addresses exchangeability violation in temporal dependencies
• Reduces prediction set size while ensuring coverage

Innovation

• End-to-end conformal prediction for temporal graphs
• Diffusion-based non-conformity score for uncertainties
• Efficiency-aware optimization algorithm improves performance