Exploring Explainability Methods for Graph Neural Networks

• URL: http://arxiv.org/abs/2211.01770v1
• Date: Thu, 3 Nov 2022 12:50:46 GMT
• Title: Exploring Explainability Methods for Graph Neural Networks
• Authors: Harsh Patel, Shivam Sahni
• Abstract summary: We demonstrate the applicability of popular explainability approaches on Graph Attention Networks (GATs) for a graph-based super-pixel image classification task. The results shed a fresh light on the notion of explainability in GNNs, particularly GATs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Graph Reasoning Networks [9.18586425686959]
  Graph Reasoning Networks (GRNs) is a novel approach to combine the strengths of fixed and learned graph representations and a reasoning module based on a differentiable satisfiability solver. Results on real-world datasets show comparable performance to GNNs. Experiments on synthetic datasets demonstrate the potential of the newly proposed method.
  arXiv  Detail & Related papers  (2024-07-08T10:53:49Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Learning How to Propagate Messages in Graph Neural Networks [55.2083896686782]
  This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs) We introduce the optimal propagation steps as latent variables to help find the maximum-likelihood estimation of the GNN parameters. Our proposed framework can effectively learn personalized and interpretable propagate strategies of messages in GNNs.
  arXiv  Detail & Related papers  (2023-10-01T15:09:59Z)
• Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective [53.999128831324576]
  Graph neural networks (GNNs) have pioneered advancements in graph representation learning. This study investigates the role of graph convolution within the context of feature learning theory.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• A Survey on Explainability of Graph Neural Networks [4.612101932762187]
  Graph neural networks (GNNs) are powerful graph-based deep-learning models. This survey aims to provide a comprehensive overview of the existing explainability techniques for GNNs.
  arXiv  Detail & Related papers  (2023-06-02T23:36:49Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Structural Explanations for Graph Neural Networks using HSIC [21.929646888419914]
  Graph neural networks (GNNs) are a type of neural model that tackle graphical tasks in an end-to-end manner. The complicated dynamics of GNNs make it difficult to understand which parts of the graph features contribute more strongly to the predictions. In this study, a flexible model agnostic explanation method is proposed to detect significant structures in graphs.
  arXiv  Detail & Related papers  (2023-02-04T09:46:47Z)
• An Empirical Study of Retrieval-enhanced Graph Neural Networks [48.99347386689936]
  Graph Neural Networks (GNNs) are effective tools for graph representation learning. We propose a retrieval-enhanced scheme called GRAPHRETRIEVAL, which is agnostic to the choice of graph neural network models. We conduct comprehensive experiments over 13 datasets, and we observe that GRAPHRETRIEVAL is able to reach substantial improvements over existing GNNs.
  arXiv  Detail & Related papers  (2022-06-01T09:59:09Z)
• Capsule Graph Neural Networks with EM Routing [8.632437524560133]
  This paper proposed novel Capsule Graph Neural Networks that use the EM routing mechanism (CapsGNNEM) to generate high-quality graph embeddings. Experimental results on a number of real-world graph datasets demonstrate that the proposed CapsGNNEM outperforms nine state-of-the-art models in graph classification tasks.
  arXiv  Detail & Related papers  (2021-10-18T06:23:37Z)
• VisGraphNet: a complex network interpretation of convolutional neural features [6.50413414010073]
  We propose and investigate the use of visibility graphs to model the feature map of a neural network. The work is motivated by an alternative viewpoint provided by these graphs over the original data.
  arXiv  Detail & Related papers  (2021-08-27T20:21:04Z)
• Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [79.28094304325116]
  Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points. We propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion.
  arXiv  Detail & Related papers  (2020-11-14T11:09:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.