Accumulated Local Effects and Graph Neural Networks for link prediction

TL;DR

This paper explores adapting Accumulated Local Effects (ALE), a model-agnostic explanation technique, to visualize feature influences in link prediction tasks using Graph Neural Networks, addressing computational challenges and comparing exact and approximate methods.

Contribution

It introduces an approximate ALE method for GNNs in link prediction, balancing explanation stability and computational efficiency, and analyzes parameter effects on estimation accuracy.

Findings

Approximate method reduces computation time significantly.

Exact method provides more stable explanations with smaller data subsets.

Parameter variations impact ALE estimation accuracy.

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 of nodes during message passing within GNN layers, complicating the direct application of ALE. Since a straightforward solution of modifying only one node at once substantially increases computation time, we propose an approximate method that mitigates this challenge. 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 with the approximate method are not significantly different from the ones obtained with the exact method. Additionally, we analyze how varying parameters affect the accuracy of ALE estimation for both approaches.