Generative Causal Explanations for Graph Neural Networks

Wanyu Lin, Hao Lan, Baochun Li
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:6666-6679, 2021.

Abstract

This paper presents {\em Gem}, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task. Then we train a causal explanation model equipped with a loss function based on Granger causality. Different from existing explainers for GNNs, {\em Gem} explains GNNs on graph-structured data from a causal perspective. It has better generalization ability as it has no requirements on the internal structure of the GNNs or prior knowledge on the graph learning tasks. In addition, {\em Gem}, once trained, can be used to explain the target GNN very quickly. Our theoretical analysis shows that several recent explainers fall into a unified framework of {\em additive feature attribution methods}. Experimental results on synthetic and real-world datasets show that {\em Gem} achieves a relative increase of the explanation accuracy by up to $30%$ and speeds up the explanation process by up to $110\times$ as compared to its state-of-the-art alternatives.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-lin21d, title = {Generative Causal Explanations for Graph Neural Networks}, author = {Lin, Wanyu and Lan, Hao and Li, Baochun}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {6666--6679}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/lin21d/lin21d.pdf}, url = {https://proceedings.mlr.press/v139/lin21d.html}, abstract = {This paper presents {\em Gem}, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task. Then we train a causal explanation model equipped with a loss function based on Granger causality. Different from existing explainers for GNNs, {\em Gem} explains GNNs on graph-structured data from a causal perspective. It has better generalization ability as it has no requirements on the internal structure of the GNNs or prior knowledge on the graph learning tasks. In addition, {\em Gem}, once trained, can be used to explain the target GNN very quickly. Our theoretical analysis shows that several recent explainers fall into a unified framework of {\em additive feature attribution methods}. Experimental results on synthetic and real-world datasets show that {\em Gem} achieves a relative increase of the explanation accuracy by up to $30%$ and speeds up the explanation process by up to $110\times$ as compared to its state-of-the-art alternatives.} }
Endnote
%0 Conference Paper %T Generative Causal Explanations for Graph Neural Networks %A Wanyu Lin %A Hao Lan %A Baochun Li %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-lin21d %I PMLR %P 6666--6679 %U https://proceedings.mlr.press/v139/lin21d.html %V 139 %X This paper presents {\em Gem}, a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. Specifically, we formulate the problem of providing explanations for the decisions of GNNs as a causal learning task. Then we train a causal explanation model equipped with a loss function based on Granger causality. Different from existing explainers for GNNs, {\em Gem} explains GNNs on graph-structured data from a causal perspective. It has better generalization ability as it has no requirements on the internal structure of the GNNs or prior knowledge on the graph learning tasks. In addition, {\em Gem}, once trained, can be used to explain the target GNN very quickly. Our theoretical analysis shows that several recent explainers fall into a unified framework of {\em additive feature attribution methods}. Experimental results on synthetic and real-world datasets show that {\em Gem} achieves a relative increase of the explanation accuracy by up to $30%$ and speeds up the explanation process by up to $110\times$ as compared to its state-of-the-art alternatives.
APA
Lin, W., Lan, H. & Li, B.. (2021). Generative Causal Explanations for Graph Neural Networks. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:6666-6679 Available from https://proceedings.mlr.press/v139/lin21d.html.

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