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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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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由于现实世界图形/网络数据中的广泛标签稀缺问题,因此,自我监督的图形神经网络(GNN)非常需要。曲线图对比度学习(GCL),通过训练GNN以其不同的增强形式最大化相同图表之间的表示之间的对应关系,即使在不使用标签的情况下也可以产生稳健和可转移的GNN。然而,GNN由传统的GCL培训经常冒险捕获冗余图形特征,因此可能是脆弱的,并在下游任务中提供子对比。在这里,我们提出了一种新的原理,称为普通的普通GCL(AD-GCL),其使GNN能够通过优化GCL中使用的对抗性图形增强策略来避免在训练期间捕获冗余信息。我们将AD-GCL与理论解释和设计基于可训练的边缘滴加图的实际实例化。我们通过与最先进的GCL方法进行了实验验证了AD-GCL,并在无监督,6 \%$ 14 \%$ 6 \%$ 14 \%$ 6 \%$ 6 \%$ 3 \%$ 3 \%$达到半监督总体学习设置,具有18个不同的基准数据集,用于分子属性回归和分类和社交网络分类。
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图形神经网络(GNNS)在广泛的应用方面显示了有希望的结果。 GNN的大多数实证研究直接将观察图视为输入,假设观察到的结构完美地描绘了节点之间的准确性和完全关系。然而,现实世界中的图形是不可避免的或不完整的,甚至可以加剧图表表示的质量。在这项工作中,我们提出了一种新颖的变分信息瓶颈引导图形结构学习框架,即vib-gsl,在信息理论的角度下。 VIB-GSL推进了图形结构学习的信息瓶颈(IB)原则,为挖掘潜在的任务关系提供了更优雅且普遍的框架。 VIB-GSL了解一个信息和压缩图形结构,用于蒸馏出特定的下游任务的可操作信息。 VIB-GSL为不规则图数据推导了变化近似,以形成促进训练稳定性的易切换IB目标函数。广泛的实验结果表明,VIB-GSL的卓越有效性和鲁棒性。
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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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图神经网络(GNN)是一类流行的机器学习模型。受到学习解释(L2X)范式的启发,我们提出了L2XGNN,这是一个可解释的GNN的框架,该框架通过设计提供了忠实的解释。L2XGNN学习了一种选择解释性子图(主题)的机制,该机制仅在GNNS消息通话操作中使用。L2XGNN能够为每个输入图选择具有特定属性的子图,例如稀疏和连接。对主题施加这种限制通常会导致更容易解释和有效的解释。几个数据集的实验表明,L2XGNN使用整个输入图实现了与基线方法相同的分类精度,同时确保仅使用提供的解释来进行预测。此外,我们表明L2XGNN能够识别负责预测图形属性的主题。
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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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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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需要解释的图表学习是需要的,因为许多科学应用都取决于学习模型来从图形结构数据中收集见解。先前的工作主要集中在使用事后方法来解释预训练的模型(尤其是图形神经网络模型)。他们反对固有的可解释模型,因为对这些模型的良好解释通常是以其预测准确性为代价。而且,广泛使用的固有解释的注意力机制通常无法在图形学习任务中提供忠实的解释。在这项工作中,我们通过提出图形随机关注(GSAT)来解决这两个问题,这是一种来自信息瓶颈原理的注意机制。 GSAT利用随机关注来阻止从任务 - 核定图组件中的信息,同时学习降低随机性的注意力以选择与任务相关的子图以进行解释。 GSAT也可以通过随机注意机制应用于微调和解释预训练的模型。八个数据集的广泛实验表明,GSAT在解释AUC中的最高最高为20%$ \ uparrow $,而预测准确性则高于最高的最高$ \ uparrow $。
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在高措施应用中大量部署图神经网络(GNNS)对对噪声的强大解释产生了强烈的需求,这些解释与人类的直觉很好。大多数现有方法通过识别与预测有很强相关性的输入图的子图来生成解释。这些解释对噪声并不强大,因为独立优化单个输入的相关性很容易过分拟合噪声。此外,它们与人类直觉并不十分吻合,因为从输入图中删除已识别的子图并不一定会改变预测结果。在本文中,我们提出了一种新颖的方法,可以通过在类似的输入图上明确建模GNNS的共同决策逻辑来生成对GNN的强大反事实解释。我们的解释自然对噪声是强大的,因为它们是由控制许多类似输入图的GNN的共同决策边界产生的。该解释也与人类的直觉很好地吻合,因为从输入图中的解释中删除了一组边缘,从而显着改变了预测。许多公共数据集上的详尽实验证明了我们方法的出色性能。
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作为图形神经网络(GNNS)在数字病理学中被广泛采用,越来越关注GNN的发出解释模型(解释器),以提高临床决策的透明度。现有的解释者发现与预测相关的解释性子图。然而,这种子图不足以揭示预测的所有关键生物学子结构,因为在去除该子图之后预测将保持不变。因此,解释性子图不仅应该需要预测,而且应该足以揭示用于解释的最具预测区域。这种解释需要测量从不同输入子图传送到预测输出的信息,我们将其定义为信息流。在这项工作中,我们解决了这些关键挑战并提出了IFExplainer,它为GNN产生了必要和充分的解释。为了评估GNN预测中的信息流,我们首先提出了一种新颖的预测性概念,命名为$ 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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在本文中,我们研究了在非全粒图上进行节点表示学习的自我监督学习的问题。现有的自我监督学习方法通​​常假定该图是同质的,其中链接的节点通常属于同一类或具有相似的特征。但是,这种同质性的假设在现实图表中并不总是正确的。我们通过为图神经网络开发脱钩的自我监督学习(DSSL)框架来解决这个问题。 DSSL模仿了节点的生成过程和语义结构的潜在变量建模的链接,该过程将不同邻域之间的不同基础语义解散到自我监督的节点学习过程中。我们的DSSL框架对编码器不可知,不需要预制的增强,因此对不同的图表灵活。为了通过潜在变量有效地优化框架,我们得出了自我监督目标的较低范围的证据,并开发了具有变异推理的可扩展培训算法。我们提供理论分析,以证明DSSL享有更好的下游性能。与竞争性的自我监督学习基线相比,对各种类图基准的广泛实验表明,我们提出的框架可以显着取得更好的性能。
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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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尽管最近在欧几里得数据(例如图像)上使用不变性原理(OOD)概括(例如图像),但有关图数据的研究仍然受到限制。与图像不同,图形的复杂性质给采用不变性原理带来了独特的挑战。特别是,图表上的分布变化可以以多种形式出现,例如属性和结构,因此很难识别不变性。此外,在欧几里得数据上通常需要的域或环境分区通常需要的图形可能非常昂贵。为了弥合这一差距,我们提出了一个新的框架,以捕获图形的不变性,以在各种分配变化下进行保证的OOD概括。具体而言,我们表征了具有因果模型的图形上的潜在分布变化,得出结论,当模型仅关注包含有关标签原因最多信息的子图时,可以实现图形上的OOD概括。因此,我们提出了一个信息理论目标,以提取最大地保留不变的阶级信息的所需子图。用这些子图学习不受分配变化的影响。对合成和现实世界数据集进行的广泛实验,包括在AI ADED药物发现中充满挑战的环境,验证了我们方法的上等OOD概括能力。
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Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}
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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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图形神经网络(GNNS)正在快速成为在多个域中学习图形结构化数据的标准方法,但它们在其决策中缺乏透明度。已经开发了几种基于扰动的方法,以提供对GNN的决策过程的见解。由于这是一个早期研究区域,用于评估生成的解释的方法和数据缺乏成熟。我们探索了这些现有的方法,并识别三个主要领域的普通缺陷:(1)合成数据生成过程,(2)评估指标,以及(3)最终呈现解释。为此目的,我们执行一个实证研究,以探索这些陷阱以及他们意外的后果,并提出补救措施来减轻它们的效果。
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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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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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