# Exploring Explainability Methods for Graph Neural Networks

Source: [Exploring Explainability Methods for Graph Neural Networks](<https://www.sota2.com/research/paper/68fa6ddd1e209131cea3c88f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:56.138Z

Authors: Harsh Patel, Shivam Sahni

Published: 2022-11-03T12:50:46.000Z

## Abstract

With the growing use of deep learning methods, particularly graph neural networks, which encode intricate interconnectedness information, for a variety of real tasks, there is a necessity for explainability in such settings. In this paper, we demonstrate the applicability of popular explainability approaches on Graph Attention Networks (GAT) for a graph-based super-pixel image classification task. We assess the qualitative and quantitative performance of these techniques on three different datasets and describe our findings. The results shed a fresh light on the notion of explainability in GNNs, particularly GATs.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.01770>)

[PDF](<https://arxiv.org/pdf/2211.01770.pdf>)