Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks

• URL: http://arxiv.org/abs/2402.04710v1
• Date: Wed, 7 Feb 2024 09:57:39 GMT
• Title: Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
• Authors: Jiahua Rao, Jiancong Xie, Hanjing Lin, Shuangjia Zheng, Zhen Wang, Yuedong Yang
• Abstract summary: We develop a novel interpretable causal GNN framework that incorporates retrieval-based causal learning with Graph Information Bottleneck (GIB) theory. We achieve 32.71% higher precision on real-world explanation scenarios with diverse explanation types.
• Score: 12.892400744247565
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Graph Neural Networks (GNNs) have gained considerable traction for their capability to effectively process topological data, yet their interpretability remains a critical concern. Current interpretation methods are dominated by post-hoc explanations to provide a transparent and intuitive understanding of GNNs. However, they have limited performance in interpreting complicated subgraphs and can't utilize the explanation to advance GNN predictions. On the other hand, transparent GNN models are proposed to capture critical subgraphs. While such methods could improve GNN predictions, they usually don't perform well on explanations. Thus, it is desired for a new strategy to better couple GNN explanation and prediction. In this study, we have developed a novel interpretable causal GNN framework that incorporates retrieval-based causal learning with Graph Information Bottleneck (GIB) theory. The framework could semi-parametrically retrieve crucial subgraphs detected by GIB and compress the explanatory subgraphs via a causal module. The framework was demonstrated to consistently outperform state-of-the-art methods, and to achieve 32.71\% higher precision on real-world explanation scenarios with diverse explanation types. More importantly, the learned explanations were shown able to also improve GNN prediction performance.

Related papers

• Explainable Graph Neural Networks Under Fire [69.15708723429307]
  Graph neural networks (GNNs) usually lack interpretability due to their complex computational behavior and the abstract nature of graphs. Most GNN explanation methods work in a post-hoc manner and provide explanations in the form of a small subset of important edges and/or nodes. In this paper we demonstrate that these explanations can unfortunately not be trusted, as common GNN explanation methods turn out to be highly susceptible to adversarial perturbations.
  arXiv  Detail & Related papers  (2024-06-10T16:09:16Z)
• 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)
• Towards Prototype-Based Self-Explainable Graph Neural Network [37.90997236795843]
  We study a novel problem of learning prototype-based self-explainable GNNs that can simultaneously give accurate predictions and prototype-based explanations on predictions. The learned prototypes are also used to simultaneously make prediction for for a test instance and provide instance-level explanation.
  arXiv  Detail & Related papers  (2022-10-05T00:47:42Z)
• Explainability in subgraphs-enhanced Graph Neural Networks [12.526174412246107]
  Subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of GNNs. In this work, we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs. We show that our framework is successful in explaining the decision process of an SGNN on graph classification tasks.
  arXiv  Detail & Related papers  (2022-09-16T13:39:10Z)
• A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics [8.795591344648294]
  We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We provide the common performance metrics for GNNs explanations and point out several future research directions.
  arXiv  Detail & Related papers  (2022-07-26T01:45:54Z)
• Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows [67.23405590815602]
  Graph Neural Networks (GNNs) are widely adopted in digital pathology. Existing explainers discover an explanatory subgraph relevant to the prediction. An explanatory subgraph should be not only necessary for prediction, but also sufficient to uncover the most predictive regions. We propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs.
  arXiv  Detail & Related papers  (2021-12-18T10:19:01Z)
• ProtGNN: Towards Self-Explaining Graph Neural Networks [12.789013658551454]
  We propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs. ProtGNN and ProtGNN+ can provide inherent interpretability while achieving accuracy on par with the non-interpretable counterparts.
  arXiv  Detail & Related papers  (2021-12-02T01:16:29Z)
• Towards Self-Explainable Graph Neural Network [24.18369781999988]
  Graph Neural Networks (GNNs) generalize the deep neural networks to graph-structured data. GNNs lack explainability, which limits their adoption in scenarios that demand the transparency of models. We propose a new framework which can find $K$-nearest labeled nodes for each unlabeled node to give explainable node classification.
  arXiv  Detail & Related papers  (2021-08-26T22:45:11Z)
• Jointly Attacking Graph Neural Network and its Explanations [50.231829335996814]
  Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs. We propose a novel attack framework (GEAttack) which can attack both a GNN model and its explanations by simultaneously exploiting their vulnerabilities.
  arXiv  Detail & Related papers  (2021-08-07T07:44:33Z)
• Parameterized Explainer for Graph Neural Network [49.79917262156429]
  We propose PGExplainer, a parameterized explainer for Graph Neural Networks (GNNs) Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting easily. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification.
  arXiv  Detail & Related papers  (2020-11-09T17:15:03Z)
• 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.