GOAt: Explaining Graph Neural Networks via Graph Output Attribution

• URL: http://arxiv.org/abs/2401.14578v1
• Date: Fri, 26 Jan 2024 00:32:58 GMT
• Title: GOAt: Explaining Graph Neural Networks via Graph Output Attribution
• Authors: Shengyao Lu, Keith G. Mills, Jiao He, Bang Liu, Di Niu
• Abstract summary: This paper introduces Graph Output Attribution (GOAt), a novel method to attribute graph outputs to input graph features. GOAt is faithful, discriminative, as well as stable across similar samples. We show that our method outperforms various state-ofthe-art GNN explainers in terms of the commonly used fidelity metric.
• Score: 32.66251068600664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for explaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. This paper introduces Graph Output Attribution (GOAt), a novel method to attribute graph outputs to input graph features, creating GNN explanations that are faithful, discriminative, as well as stable across similar samples. By expanding the GNN as a sum of scalar products involving node features, edge features and activation patterns, we propose an efficient analytical method to compute contribution of each node or edge feature to each scalar product and aggregate the contributions from all scalar products in the expansion form to derive the importance of each node and edge. Through extensive experiments on synthetic and real-world data, we show that our method not only outperforms various state-ofthe-art GNN explainers in terms of the commonly used fidelity metric, but also exhibits stronger discriminability, and stability by a remarkable margin.

Related papers

• What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding [0.0]
  We aim to deepen the theoretical understanding of Graph Neural Networks (GNNs) on large graphs, with a focus on their expressive power. Recently, several works showed that, on very general random graphs models, GNNs converge to certains functions as the number of nodes grows.
  arXiv  Detail & Related papers  (2023-05-24T07:09:53Z)
• 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)
• Faithful Explanations for Deep Graph Models [44.3056871040946]
  This paper studies faithful explanations for Graph Neural Networks (GNNs) It applies to existing explanation methods, including feature attributions and subgraph explanations. Third, we introduce emphk-hop Explanation with a Convolutional Core (KEC), a new explanation method that provably maximizes faithfulness to the original GNN.
  arXiv  Detail & Related papers  (2022-05-24T07:18:56Z)
• Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis [124.97061497512804]
  We show that standard Graph Neural Networks (GNNs) produce more discriminative representations than the Weisfeiler-Lehman (WL) algorithm. We also show that simple convolutional architectures with white inputs, produce equivariant features that count the closed paths in the graph.
  arXiv  Detail & Related papers  (2022-05-19T18:40:25Z)
• Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification [59.06717774425588]
  We propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field. It contains explicit pairwise factors to model output-output relations and uses a GNN backbone to model input-output relations. We conduct experiments on various datasets, which shows that our model can effectively improve the performance for semi-supervised node classification on graphs.
  arXiv  Detail & Related papers  (2021-07-27T19:47:53Z)
• GraphSVX: Shapley Value Explanations for Graph Neural Networks [81.83769974301995]
  Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data. In this paper, we propose a unified framework satisfied by most existing GNN explainers. We introduce GraphSVX, a post hoc local model-agnostic explanation method specifically designed for GNNs.
  arXiv  Detail & Related papers  (2021-04-18T10:40:37Z)
• A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
  Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem. We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
  arXiv  Detail & Related papers  (2020-10-05T04:57:18Z)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
  Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information. GNNs are mostly treated as black-boxes and lack human intelligible explanations. We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
  arXiv  Detail & Related papers  (2020-06-03T23:52:43Z)

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.