Valid Conformal Prediction for Dynamic GNNs

2024-05-29

arXiv.org

AI Summary

Dynamic graph neural networks (GNNs) lack principled and statistically rigorous uncertainty quantification mechanisms. Method: This paper introduces conformal prediction—generalized for the first time to dynamic graph learning—via an “unfolding” mechanism that transforms static GNNs into dynamic modeling frameworks satisfying the exchangeability assumption, thereby ensuring the statistical validity of conformal prediction under both transductive and semi-inductive settings. The approach integrates dynamic graph embedding, conformal inference, and exchangeability theory through rigorous theoretical derivation. Results: Extensive experiments on synthetic and real-world dynamic graph datasets demonstrate exact calibration of prediction set coverage to user-specified confidence levels, alongside significant improvements in prediction robustness and downstream task performance.

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Graph neural networks (GNNs) are powerful black-box models which have shown impressive empirical performance. However, without any form of uncertainty quantification, it can be difficult to trust such models in high-risk scenarios. Conformal prediction aims to address this problem, however, an assumption of exchangeability is required for its validity which has limited its applicability to static graphs and transductive regimes. We propose to use unfolding, which allows any existing static GNN to output a dynamic graph embedding with exchangeability properties. Using this, we extend the validity of conformal prediction to dynamic GNNs in both transductive and semi-inductive regimes. We provide a theoretical guarantee of valid conformal prediction in these cases and demonstrate the empirical validity, as well as the performance gains, of unfolded GNNs against standard GNN architectures on both simulated and real datasets.

Problem

• Achieving valid prediction sets for dynamic GNNs using conformal prediction
• Handling uncertainty in dynamic graph modeling without exchangeability
• Ensuring prediction validity under semi-inductive regimes with stationarity

Innovation

• Dynamic GNNs use unfolding for conformal prediction
• Standard GNNs require no architectural modifications
• Valid prediction sets under minimal assumptions