Valid Conformal Prediction for Dynamic GNNs

• URL: http://arxiv.org/abs/2405.19230v2
• Date: Wed, 26 Mar 2025 11:54:52 GMT
• Title: Valid Conformal Prediction for Dynamic GNNs
• Authors: Ed Davis, Ian Gallagher, Daniel John Lawson, Patrick Rubin-Delanchy,
• Abstract summary: We use a dynamic graph representation known in the tensor literature as the unfolding to achieve valid prediction sets via conformal prediction.<n>One of our key contributions is a careful mathematical consideration of the different inference scenarios which can arise in a dynamic graph modelling context.<n>We provide real data examples demonstrating validity, showing improved accuracy over baselines, and sign-posting different failure modes.
• Score: 5.64979077798699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic graphs provide a flexible data abstraction for modelling many sorts of real-world systems, such as transport, trade, and social networks. Graph neural networks (GNNs) are powerful tools allowing for different kinds of prediction and inference on these systems, but getting a handle on uncertainty, especially in dynamic settings, is a challenging problem. In this work we propose to use a dynamic graph representation known in the tensor literature as the unfolding, to achieve valid prediction sets via conformal prediction. This representation, a simple graph, can be input to any standard GNN and does not require any modification to existing GNN architectures or conformal prediction routines. One of our key contributions is a careful mathematical consideration of the different inference scenarios which can arise in a dynamic graph modelling context. For a range of practically relevant cases, we obtain valid prediction sets with almost no assumptions, even dispensing with exchangeability. In a more challenging scenario, which we call the semi-inductive regime, we achieve valid prediction under stronger assumptions, akin to stationarity. We provide real data examples demonstrating validity, showing improved accuracy over baselines, and sign-posting different failure modes which can occur when those assumptions are violated.

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