  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.
