BAGEL: A Benchmark for Assessing Graph Neural Network Explanations

TL;DR

This paper introduces Bagel, a comprehensive benchmark for evaluating explainability methods for graph neural networks across multiple datasets and evaluation regimes, facilitating standardized assessment of interpretability approaches.

Contribution

It proposes four diverse evaluation regimes for GNN explanations and provides a unified benchmark with reference implementations for the community.

Findings

Extensive empirical study on four GNN models and nine explanation methods.

Reconciliation of multiple evaluation metrics for GNN interpretability.

Benchmark and code publicly available for reproducibility.

Abstract

The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with graph data called graph neural networks. Evaluating interpretability approaches for graph neural networks (GNN) specifically are known to be challenging due to the lack of a commonly accepted benchmark. Given a GNN model, several interpretability approaches exist to explain GNN models with diverse (sometimes conflicting) evaluation methodologies. In this paper, we propose a benchmark for evaluating the explainability approaches for GNNs called Bagel. In Bagel, we firstly propose four diverse GNN explanation evaluation regimes -- 1) faithfulness, 2) sparsity, 3) correctness. and 4) plausibility. We reconcile multiple evaluation metrics in the existing literature and cover diverse notions for a holistic evaluation. Our graph datasets range from citation networks, document graphs, to graphs from molecules and proteins. We conduct an extensive empirical study on four GNN models and nine post-hoc explanation approaches for node and graph classification tasks. We open both the benchmarks and reference implementations and make them available at https://github.com/Mandeep-Rathee/Bagel-benchmark.