Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning

• URL: http://arxiv.org/abs/2406.13869v1
• Date: Wed, 19 Jun 2024 22:16:40 GMT
• Title: Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
• Authors: Danqing Wang, Antonis Antoniades, Kha-Dinh Luong, Edwin Zhang, Mert Kosan, Jiachen Li, Ambuj Singh, William Yang Wang, Lei Li,
• Abstract summary: We develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. The global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets.
• Score: 49.095065258759895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations or rules that can better explain the high-level properties of the models and data in question. However, evaluating global counterfactual explanations is hard in real-world datasets due to a lack of human-annotated ground truth, which limits their use in areas like molecular sciences. Additionally, the increasing scale of these datasets provides a challenge for random search-based methods. In this paper, we develop a novel global explanation model RLHEX for molecular property prediction. It aligns the counterfactual explanations with human-defined principles, making the explanations more interpretable and easy for experts to evaluate. RLHEX includes a VAE-based graph generator to generate global explanations and an adapter to adjust the latent representation space to human-defined principles. Optimized by Proximal Policy Optimization (PPO), the global explanations produced by RLHEX cover 4.12% more input graphs and reduce the distance between the counterfactual explanation set and the input set by 0.47% on average across three molecular datasets. RLHEX provides a flexible framework to incorporate different human-designed principles into the counterfactual explanation generation process, aligning these explanations with domain expertise. The code and data are released at https://github.com/dqwang122/RLHEX.

Related papers

• Global Concept Explanations for Graphs by Contrastive Learning [0.6906005491572401]
  We propose a method to extract global concept explanations from the predictions of graph neural networks. We conduct computational experiments on synthetic and real-world graph property prediction tasks.
  arXiv  Detail & Related papers  (2024-04-25T11:43:46Z)
• XInsight: Revealing Model Insights for GNNs with Flow-based Explanations [0.0]
  Many high-stakes applications, such as drug discovery, require human-intelligible explanations from the models. We propose an explainability algorithm for GNNs called XInsight that generates a distribution of model explanations using GFlowNets. We show the utility of XInsight's explanations by analyzing the generated compounds using QSAR modeling.
  arXiv  Detail & Related papers  (2023-06-07T21:25:32Z)
• Robust Ante-hoc Graph Explainer using Bilevel Optimization [0.7999703756441758]
  We propose RAGE, a novel and flexible ante-hoc explainer for graph neural networks. RAGE can effectively identify molecular substructures that contain the full information needed for prediction. Our experiments on various molecular classification tasks show that RAGE explanations are better than existing post-hoc and ante-hoc approaches.
  arXiv  Detail & Related papers  (2023-05-25T05:50:38Z)
• Evaluating Explainability for Graph Neural Networks [21.339111121529815]
  We introduce a synthetic graph data generator, ShapeGGen, which can generate a variety of benchmark datasets. We include ShapeGGen and several real-world graph datasets into an open-source graph explainability library, GraphXAI.
  arXiv  Detail & Related papers  (2022-08-19T13:43:52Z)
• Towards Explanation for Unsupervised Graph-Level Representation Learning [108.31036962735911]
  Existing explanation methods focus on the supervised settings, eg, node classification and graph classification, while the explanation for unsupervised graph-level representation learning is still unexplored. In this paper, we advance the Information Bottleneck principle (IB) to tackle the proposed explanation problem for unsupervised graph representations, which leads to a novel principle, textitUnsupervised Subgraph Information Bottleneck (USIB) We also theoretically analyze the connection between graph representations and explanatory subgraphs on the label space, which reveals that the robustness of representations benefit the fidelity of explanatory subgraphs.
  arXiv  Detail & Related papers  (2022-05-20T02:50:15Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Discovering Invariant Rationales for Graph Neural Networks [104.61908788639052]
  Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input graph's features. We propose a new strategy of discovering invariant rationale (DIR) to construct intrinsically interpretable GNNs.
  arXiv  Detail & Related papers  (2022-01-30T16:43:40Z)
• GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks [0.3441021278275805]
  GCExplainer is an unsupervised approach for post-hoc discovery and extraction of global concept-based explanations for graph neural networks (GNNs) We demonstrate the success of our technique on five node classification datasets and two graph classification datasets, showing that we are able to discover and extract high-quality concept representations by putting the human in the loop.
  arXiv  Detail & Related papers  (2021-07-25T20:52:48Z)
• 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)
• 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)

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.