TY - JOUR
T1 - ZORRO
T2 - Valid, Sparse, and Stable Explanations in Graph Neural Networks
AU - Funke, Thorben
AU - Khosla, Megha
AU - Rathee, Mandeep
AU - Anand, Avishek
N1 - Funding Information:
This work was partially supported in part by the project "CampaNeo" under Grant ID 01MD19007
PY - 2022/8/24
Y1 - 2022/8/24
N2 - With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network. Explanations for graph neural networks differ in principle from other input settings. It is important to attribute the decision to input features and other related instances connected by the graph structure. We find that the previous explanation generation approaches that maximize the mutual information between the label distribution produced by the model and the explanation to be restrictive. Specifically, existing approaches do not enforce explanations to be valid, sparse, or robust to input perturbations. In this paper, we lay down some of the fundamental principles that an explanation method for graph neural networks should follow and introduce a metric RDT-Fidelity as a measure of the explanation's effectiveness. We propose a novel approach Zorro based on the principles from rate-distortion theory that uses a simple combinatorial procedure to optimize for RDT-Fidelity. Extensive experiments on real and synthetic datasets reveal that Zorro produces sparser, stable, and more faithful explanations than existing graph neural network explanation approaches.
AB - With the ever-increasing popularity and applications of graph neural networks, several proposals have been made to explain and understand the decisions of a graph neural network. Explanations for graph neural networks differ in principle from other input settings. It is important to attribute the decision to input features and other related instances connected by the graph structure. We find that the previous explanation generation approaches that maximize the mutual information between the label distribution produced by the model and the explanation to be restrictive. Specifically, existing approaches do not enforce explanations to be valid, sparse, or robust to input perturbations. In this paper, we lay down some of the fundamental principles that an explanation method for graph neural networks should follow and introduce a metric RDT-Fidelity as a measure of the explanation's effectiveness. We propose a novel approach Zorro based on the principles from rate-distortion theory that uses a simple combinatorial procedure to optimize for RDT-Fidelity. Extensive experiments on real and synthetic datasets reveal that Zorro produces sparser, stable, and more faithful explanations than existing graph neural network explanation approaches.
KW - Computational modeling
KW - Data models
KW - Explainability
KW - Feature extraction
KW - Graph Neural Networks
KW - Graph neural networks
KW - Interpretability
KW - Rate-distortion
KW - Stability analysis
KW - Task analysis
KW - graph neural networks
KW - interpretability
UR - http://www.scopus.com/inward/record.url?scp=85137574213&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2105.08621
DO - 10.48550/arXiv.2105.08621
M3 - Article
AN - SCOPUS:85137574213
VL - 35
SP - 8687
EP - 8698
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
SN - 1041-4347
IS - 8
ER -