作为图形神经网络(GNNS)在数字病理学中被广泛采用,越来越关注GNN的发出解释模型(解释器),以提高临床决策的透明度。现有的解释者发现与预测相关的解释性子图。然而,这种子图不足以揭示预测的所有关键生物学子结构,因为在去除该子图之后预测将保持不变。因此,解释性子图不仅应该需要预测,而且应该足以揭示用于解释的最具预测区域。这种解释需要测量从不同输入子图传送到预测输出的信息,我们将其定义为信息流。在这项工作中,我们解决了这些关键挑战并提出了IFExplainer,它为GNN产生了必要和充分的解释。为了评估GNN预测中的信息流,我们首先提出了一种新颖的预测性概念,命名为$ F $ -Information,它是定向的,并包含GNN模型的现实容量。基于它,IFExplainer产生具有最大信息流到预测的解释性子图。同时,在去除解释之后,它最小化了从输入到预测结果的信息流。因此,所产生的解释对于预测并且足以揭示最重要的子结构是重要的。我们评估IFExplainer以解释GNN对乳腺癌亚型的预测。 BRACS数据集的实验结果显示了该方法的卓越性能。
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子图识别旨在发现对图表属性最具信息的图表的压缩子结构。可以通过使用相互信息估计器优化图形信息瓶颈(GIB)来配制它。然而,由于图数据的相互信息本质上难以估计,GIB遭到培训不稳定。本文介绍了一种噪声注入方法,用于压缩子图中的信息,这导致了一种新颖的变分图信息瓶颈(VGIB)框架。VGIB允许对其在温和假设下的物镜的易变分别近似。因此,VGIB享有更稳定和高效的培训过程 - 我们发现VGIB在练习中提高表演的速度快10倍。广泛的图形解释实验,图形神经网络的解释性,图表分类显示VGIB发现比现有方法更好的子图。
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由于图形神经网络(GNN)在各个域中的出色性能,因此对GNN解释问题越来越感兴趣“ \ emph {输入图的哪一部分是决定模型决定的最关键?}“现有的解释?方法集中在监督的设置,例如节点分类和图形分类上,而无监督的图形表示学习的解释仍未探索。当部署高级决策情况时,图表表示的不透明可能会导致意外风险。在本文中,我们推进了信息瓶颈原理(IB),以解决无监督的图表表示所提出的解释问题,这导致了一个新颖的原理,\ textit {无监督的子图表信息瓶颈}(USIB)。我们还理论上分析了标签空间上图表和解释子图之间的联系,这表明表示的表现力和鲁棒性有益于解释性子图的保真度。合成和现实世界数据集的实验结果证明了我们发达的解释器的优越性以及我们的理论分析的有效性。
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图形神经网络(GNN)已证明图形数据的预测性能显着提高。同时,这些模型的预测通常很难解释。在这方面,已经做出了许多努力来从gnnexplainer,XGNN和PGEXPlainer等角度解释这些模型的预测机制。尽管这样的作品呈现出系统的框架来解释GNN,但对于可解释的GNN的整体评论是不可用的。在这项调查中,我们介绍了针对GNN开发的解释性技术的全面综述。我们专注于可解释的图形神经网络,并根据可解释方法的使用对它们进行分类。我们进一步为GNNS解释提供了共同的性能指标,并指出了几个未来的研究指标。
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深度学习方法正在实现许多人工智能任务上的不断增长。深层模型的一个主要局限性是它们不适合可解释性。可以通过开发事后技术来解释预测,从而产生解释性领域,从而规避这种限制。最近,关于图像和文本的深层模型的解释性取得了重大进展。在图数据的领域,图形神经网络(GNN)及其解释性正在迅速发展。但是,既没有对GNN解释性方法的统一处理,也没有标准的基准和测试床。在这项调查中,我们提供了当前GNN解释性方法的统一和分类观点。我们对这一主题的统一和分类治疗对现有方法的共同性和差异阐明了灯光,并为进一步的方法论发展奠定了基础。为了促进评估,我们生成了一组专门用于GNN解释性的基准图数据集。我们总结了当前的数据集和指标,以评估GNN的解释性。总的来说,这项工作提供了GNN解释性和评估标准化测试床的统一方法论。
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Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models and explaining predictions made by GNNs remains unsolved. Here we propose GNNEXPLAINER, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Given an instance, GNNEXPLAINER identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction. Further, GNNEXPLAINER can generate consistent and concise explanations for an entire class of instances. We formulate GNNEXPLAINER as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures. Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms alternative baseline approaches by up to 43.0% in explanation accuracy. GNNEXPLAINER provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty GNNs.
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我们研究了图神经网络(GNN)的解释性,作为阐明其工作机制的一步。尽管大多数当前方法都集中在解释图节点,边缘或功能上,但我们认为,作为GNNS的固有功能机制,消息流对执行解释性更为自然。为此,我们在这里提出了一种新颖的方法,即FlowX,以通过识别重要的消息流来解释GNN。为了量化流量的重要性,我们建议遵循合作游戏理论中沙普利价值观的哲学。为了解决计算所有联盟边际贡献的复杂性,我们提出了一个近似方案,以计算类似沙普利的值,作为进一步再分配训练的初步评估。然后,我们提出一种学习算法来训练流量评分并提高解释性。关于合成和现实世界数据集的实验研究表明,我们提出的FlowX导致GNN的解释性提高。
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图形神经网络(GNN)已成为编码图形结构数据的强大工具。由于其广泛的应用程序,越来越需要开发工具来解释GNN如何做出给定的图形结构数据决定。现有的基于学习的GNN解释方法在培训中是特定于任务的,因此遭受了关键的缺点。具体而言,它们无法为使用单个解释器提供多任务预测模型的解释。在GNN以自我监督的方式训练的情况下,他们也无法提供解释,并且在未来的下游任务中使用了结果表示。为了解决这些局限性,我们提出了一个任务不合时宜的GNN解释器(TAGE),该解释器(Tage)独立于下游模型,并在自学人员的情况下接受了训练,而对下游任务不了解。 Tage可以通过看不见的下游任务来解释GNN嵌入模型,并可以有效解释多任务模型。我们的广泛实验表明,通过使用相同的模型来解释多个下游任务的预测,同时实现了与当前最新的GNN解释方法一样好甚至更好的解释质量,可以显着提高解释效率。我们的代码可公开作为DIG库的一部分,网址为https://github.com/divelab/dig/tree/main/main/dig/xgraph/tage/。
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解释机器学习决策的问题是经过深入研究和重要的。我们对一种涉及称为图形神经网络的图形数据的特定类型的机器学习模型感兴趣。众所周知,由于缺乏公认的基准,评估图形神经网络(GNN)的可解释性方法是具有挑战性的。鉴于GNN模型,存在几种可解释性方法来解释具有多种(有时相互矛盾的)方法论的GNN模型。在本文中,我们提出了一个基准,用于评估称为Bagel的GNN的解释性方法。在百吉饼中,我们首先提出了四种不同的GNN解释评估制度 - 1)忠诚,2)稀疏性,3)正确性。 4)合理性。我们在现有文献中调和多个评估指标,并涵盖了各种概念以进行整体评估。我们的图数据集范围从引文网络,文档图,到分子和蛋白质的图。我们对四个GNN模型和九个有关节点和图形分类任务的事后解释方法进行了广泛的实证研究。我们打开基准和参考实现,并在https://github.com/mandeep-rathee/bagel-benchmark上提供它们。
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With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification the explainability of GNNs has become an indispensable component for predictive and trustworthy decision-making. Thus, it is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. Some GNNs explainers have been proposed recently. However, they lack to generate accurate and real explanations. To mitigate these limitations, we propose GANExplainer, based on Generative Adversarial Network (GAN) architecture. GANExplainer is composed of a generator to create explanations and a discriminator to assist with the Generator development. We investigate the explanation accuracy of our models by comparing the performance of GANExplainer with other state-of-the-art methods. Our empirical results on synthetic datasets indicate that GANExplainer improves explanation accuracy by up to 35\% compared to its alternatives.
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Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over recent years. Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions. Though various algorithms are proposed, most of them formalize this task by searching the minimal subgraph which can preserve original predictions. However, an inductive bias is deep-rooted in this framework: several subgraphs can result in the same or similar outputs as the original graphs. Consequently, they have the danger of providing spurious explanations and fail to provide consistent explanations. Applying them to explain weakly-performed GNNs would further amplify these issues. To address this problem, we theoretically examine the predictions of GNNs from the causality perspective. Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input. Observing that both confounding effects and diverse causal rationales are encoded in internal representations, we propose a simple yet effective countermeasure by aligning embeddings. Concretely, concerning potential shifts in the high-dimensional space, we design a distribution-aware alignment algorithm based on anchors. This new objective is easy to compute and can be incorporated into existing techniques with no or little effort. Theoretical analysis shows that it is in effect optimizing a more faithful explanation objective in design, which further justifies the proposed approach.
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图形神经网络(GNN)在各种高桩预测任务中实现了最先进的性能,但是具有不规则结构的图表上的多层聚合使得GNN成为一种更不可解释的模型。先前的方法使用更简单的子图来模拟完整模型,或识别预测原因的完整模型或反事实。这两个方法旨在瞄准两个不同的目标,“模拟性”和“反事实相关”,但目前尚不清楚目标如何共同影响人类理解解释。我们设计用户学习,以调查这些关节效果,并使用该研究结果设计多目标优化(MOO)算法,以查找帕累托最佳解释,可在模拟性和反事实方面得到良好平衡。由于目标模型可以是任何GNN变体,并且由于隐私问题可能无法访问,因此我们使用零顺序信息设计一个搜索算法而不访问目标模型的架构和参数。来自四个应用的九个图表的定量实验表明,帕累托有效的解释主导使用一阶连续优化或离散组合搜索的单目标基线。在鲁棒性和敏感性中进一步评估了解释,以表明他们揭示令人信服的令人信服的能力,同时对可能的混乱持谨慎态度。各种主导的反事件可以证明算法追索权的可行性,这可能促进人类参与使用GNN决策的算法公平性。
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在高措施应用中大量部署图神经网络(GNNS)对对噪声的强大解释产生了强烈的需求,这些解释与人类的直觉很好。大多数现有方法通过识别与预测有很强相关性的输入图的子图来生成解释。这些解释对噪声并不强大,因为独立优化单个输入的相关性很容易过分拟合噪声。此外,它们与人类直觉并不十分吻合,因为从输入图中删除已识别的子图并不一定会改变预测结果。在本文中,我们提出了一种新颖的方法,可以通过在类似的输入图上明确建模GNNS的共同决策逻辑来生成对GNN的强大反事实解释。我们的解释自然对噪声是强大的,因为它们是由控制许多类似输入图的GNN的共同决策边界产生的。该解释也与人类的直觉很好地吻合,因为从输入图中的解释中删除了一组边缘,从而显着改变了预测。许多公共数据集上的详尽实验证明了我们方法的出色性能。
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With the increasing use of Graph Neural Networks (GNNs) in critical real-world applications, several post hoc explanation methods have been proposed to understand their predictions. However, there has been no work in generating explanations on the fly during model training and utilizing them to improve the expressive power of the underlying GNN models. In this work, we introduce a novel explanation-directed neural message passing framework for GNNs, EXPASS (EXplainable message PASSing), which aggregates only embeddings from nodes and edges identified as important by a GNN explanation method. EXPASS can be used with any existing GNN architecture and subgraph-optimizing explainer to learn accurate graph embeddings. We theoretically show that EXPASS alleviates the oversmoothing problem in GNNs by slowing the layer wise loss of Dirichlet energy and that the embedding difference between the vanilla message passing and EXPASS framework can be upper bounded by the difference of their respective model weights. Our empirical results show that graph embeddings learned using EXPASS improve the predictive performance and alleviate the oversmoothing problems of GNNs, opening up new frontiers in graph machine learning to develop explanation-based training frameworks.
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作为当今最受欢迎的机器学习模型之一,Graph神经网络(GNN)最近引起了激烈的兴趣,其解释性也引起了人们的兴趣。用户对更好地了解GNN模型及其结果越来越感兴趣。不幸的是,当今的GNN评估框架通常依赖于合成数据集,从而得出有限范围的结论,因为问题实例缺乏复杂性。由于GNN模型被部署到更关键的任务应用程序中,因此我们迫切需要使用GNN解释性方法的共同评估协议。在本文中,据我们最大的知识,我们提出了针对GNN解释性的第一个系统评估框架,考虑了三种不同的“用户需求”的解释性:解释焦点,掩盖性质和掩蔽转换。我们提出了一个独特的指标,该指标将忠诚度措施结合在一起,并根据其足够或必要的质量对解释进行分类。我们将自己范围用于节点分类任务,并比较GNN的输入级解释性领域中最具代表性的技术。对于广泛使用的合成基准测试,令人惊讶的是,诸如个性化Pagerank之类的浅水技术在最小计算时间内具有最佳性能。但是,当图形结构更加复杂并且节点具有有意义的特征时,根据我们的评估标准,基于梯度的方法,尤其是显着性。但是,没有人在所有评估维度上占主导地位,而且总会有一个权衡。我们在eBay图上的案例研究中进一步应用了我们的评估协议,以反映生产环境。
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Explainability of Graph Neural Networks (GNNs) is critical to various GNN applications but remains an open challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a trade-off between the two. To search for the most necessary and sufficient explanation, the Probability of Necessity and Sufficiency (PNS) can be applied since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limits its wide use. To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound. Specifically, we employ nearest neighbor matching to generate counterfactual samples for the features, which is different from the random perturbation. In particular, NSEG combines the edges and node features to generate an explanation, where the common edge explanation is a special case of the combined explanation. Empirical study shows that NSEG achieves excellent performance in generating the most necessary and sufficient explanations among a series of state-of-the-art methods.
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In this paper, we investigate the degree of explainability of graph neural networks (GNNs). Existing explainers work by finding global/local subgraphs to explain a prediction, but they are applied after a GNN has already been trained. Here, we propose a meta-learning framework for improving the level of explainability of a GNN directly at training time, by steering the optimization procedure towards what we call `interpretable minima'. Our framework (called MATE, MetA-Train to Explain) jointly trains a model to solve the original task, e.g., node classification, and to provide easily processable outputs for downstream algorithms that explain the model's decisions in a human-friendly way. In particular, we meta-train the model's parameters to quickly minimize the error of an instance-level GNNExplainer trained on-the-fly on randomly sampled nodes. The final internal representation relies upon a set of features that can be `better' understood by an explanation algorithm, e.g., another instance of GNNExplainer. Our model-agnostic approach can improve the explanations produced for different GNN architectures and use any instance-based explainer to drive this process. Experiments on synthetic and real-world datasets for node and graph classification show that we can produce models that are consistently easier to explain by different algorithms. Furthermore, this increase in explainability comes at no cost for the accuracy of the model.
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Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to compute feature importance towards model predictions on images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In this work, we revisit the appropriateness of the Shapley value for GNN explanation, where the task is to identify the most important subgraph and constituent nodes for GNN predictions. We claim that the Shapley value is a non-ideal choice for graph data because it is by definition not structure-aware. We propose a Graph Structure-aware eXplanation (GStarX) method to leverage the critical graph structure information to improve the explanation. Specifically, we define a scoring function based on a new structure-aware value from the cooperative game theory proposed by Hamiache and Navarro (HN). When used to score node importance, the HN value utilizes graph structures to attribute cooperation surplus between neighbor nodes, resembling message passing in GNNs, so that node importance scores reflect not only the node feature importance, but also the node structural roles. We demonstrate that GStarX produces qualitatively more intuitive explanations, and quantitatively improves explanation fidelity over strong baselines on chemical graph property prediction and text graph sentiment classification.
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图形神经网络(GNN)表现出令人满意的各种图分析问题的性能。因此,在各种决策方案中,它们已成为\ emph {de exto}解决方案。但是,GNN可以针对某些人口亚组产生偏差的结果。最近的一些作品在经验上表明,输入网络的偏见结构是GNN的重要来源。然而,没有系统仔细检查输入网络结构的哪一部分会导致对任何给定节点的偏见预测。对输入网络的结构如何影响GNN结果的偏见的透明度很大,在很大程度上限制了在各种决策方案中的安全采用GNN。在本文中,我们研究了GNN中偏见的结构解释的新研究问题。具体而言,我们提出了一个新颖的事后解释框架,以识别可以最大程度地解释出偏见的两个边缘集,并最大程度地促进任何给定节点的GNN预测的公平水平。这种解释不仅提供了对GNN预测的偏见/公平性的全面理解,而且在建立有效但公平的GNN模型方面具有实际意义。对现实世界数据集的广泛实验验证了拟议框架在为GNN偏见提供有效的结构解释方面的有效性。可以在https://github.com/yushundong/referee上找到开源代码。
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本文着重于由于看不见的分布变化而导致性能下降的图表上的分布概括。以前的图形域概括始终诉诸于不同源域之间的不变预测因子。但是,他们假设在培训期间提供了足够的源域,为现实应用带来了巨大挑战。相比之下,我们通过从源域中构造多个种群来提出一个新的图形域概括框架,称为DPS。具体而言,DPS旨在发现多个\ textbf {d} iverse和\ textbf {p}可redictable \ textbf {s}带有一组发电机的ubgraphs,即,子图是彼此不同的,但它们彼此不同,但所有这些都与相同的语义共享输入图。这些生成的源域被利用以学习跨域的\ textIt {Equi-Prestivical}图神经网络(GNN),这有望很好地概括到看不见的目标域。通常,DPS是模型不合时宜的,可以与各种GNN骨架合并。节点级别和图形基准测试的广泛实验表明,所提出的DPS为各种图形域概括任务实现了令人印象深刻的性能。
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