Accumulated local effects and graph neural networks for link prediction

TL;DR

This paper explores using ALE explanations with GNNs to understand node features in link prediction tasks.

Contribution

The novel contribution is adapting ALE for GNNs and proposing an approximate method to reduce computation time.

Findings

The approximate ALE method is computationally efficient but less stable than the exact method.

Explanations from the approximate method are not significantly different from the exact method.

Parameter variations affect ALE estimation accuracy differently for each method.

Abstract

We investigate how Accumulated Local Effects (ALE), a model-agnostic explanation method, can be adapted to visualize the influence of node feature values in link prediction tasks using Graph Neural Networks (GNNs), specifically Graph Convolutional Networks and Graph Attention Networks. A key challenge addressed in this work is the complex interactions among nodes during message passing within GNN layers, which complicate the direct application of ALE. Since a straightforward solution of modifying only one node at a time substantially increases computation time, we propose an approximate method that mitigates this issue. Our findings reveal that although the approximate method offers computational efficiency, the exact method yields more stable explanations, particularly when smaller data subsets are used. However, the explanations produced by the approximate method are not significantly different from those obtained by the exact method. Additionally, we analyze how varying parameters affect the accuracy of ALE estimation for both approaches.

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