Towards Faithful and Consistent Explanations for Graph Neural Networks

Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph which can preserve original predictions. However, an inductive bias is deep-rooted in this framework: several subgraphs can result in the same or similar outputs as the original graphs. Consequently, they have the danger of providing spurious explanations and fail to provide consistent explanations. Applying them to explain weakly-performed GNNs would further amplify these issues. To address this problem, we theoretically examine the predictions of GNNs from the causality perspective. Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input. Observing that both confounding effects and diverse causal rationales are encoded in internal representations, we propose a simple yet effective countermeasure by aligning embeddings. Concretely, concerning potential shifts in the high-dimensional space, we design a distribution-aware alignment algorithm based on anchors. This new objective is easy to compute and can be incorporated into existing techniques with no or little effort. Theoretical analysis shows that it is in effect optimizing a more faithful explanation objective in design, which further justifies the proposed approach.

Original languageEnglish (US)
Title of host publicationWSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages634-642
Number of pages9
ISBN (Electronic)9781450394079
DOIs
StatePublished - Feb 27 2023
Event16th ACM International Conference on Web Search and Data Mining, WSDM 2023 - Singapore, Singapore
Duration: Feb 27 2023Mar 3 2023

Publication series

NameWSDM 2023 - Proceedings of the 16th ACM International Conference on Web Search and Data Mining

Conference

Conference16th ACM International Conference on Web Search and Data Mining, WSDM 2023
Country/TerritorySingapore
CitySingapore
Period2/27/233/3/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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