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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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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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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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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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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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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Many applications of machine learning require a model to make accurate predictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and then fine-tune it on a downstream task of interest. While pre-training has been effective in many language and vision domains, it remains an open question how to effectively use pre-training on graph datasets. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. We systematically study pre-training on multiple graph classification datasets. We find that naïve strategies, which pre-train GNNs at the level of either entire graphs or individual nodes, give limited improvement and can even lead to negative transfer on many downstream tasks. In contrast, our strategy avoids negative transfer and improves generalization significantly across downstream tasks, leading up to 9.4% absolute improvements in ROC-AUC over non-pre-trained models and achieving state-of-the-art performance for molecular property prediction and protein function prediction.However, pre-training on graph datasets remains a hard challenge. Several key studies (
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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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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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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 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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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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使用图神经网络(GNN)提取分子的信息表示,对于AI驱动的药物发现至关重要。最近,图形研究界一直在试图复制自然语言处理预处理的成功,并获得了一些成功。但是,我们发现在许多情况下,自我监督预审计对分子数据的益处可以忽略不计。我们对GNN预处理的关键组成部分进行了彻底的消融研究,包括预处理目标,数据拆分方法,输入特征,预处理数据集量表和GNN体系结构,以决定下游任务的准确性。我们的第一个重要发现是,在许多情况下,自我监督的图表预处理没有统计学上的显着优势。其次,尽管可以通过额外的监督预处理可以观察到改进,但通过更丰富或更平衡的数据拆分,改进可能会减少。第三,实验性超参数对下游任务的准确性具有更大的影响,而不是训练训练的任务。我们假设对分子进行预训练的复杂性不足,从而导致下游任务的可转移知识较低。
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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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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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尽管近期图形神经网络(GNN)进展,但解释了GNN的预测仍然具有挑战性。现有的解释方法主要专注于后性后解释,其中采用另一种解释模型提供培训的GNN的解释。后HOC方法未能揭示GNN的原始推理过程的事实引发了建立GNN与内置解释性的需求。在这项工作中,我们提出了原型图形神经网络(Protgnn),其将原型学习与GNNS相结合,并提供了对GNN的解释的新视角。在Protgnn中,解释自然地从基于案例的推理过程衍生,并且实际在分类期间使用。通过将输入与潜伏空间中的一些学习原型的输入进行比较来获得ProtGnn的预测。此外,为了更好地解释性和更高的效率,结合了一种新颖的条件子图采样模块,以指示输入图的哪个部分与ProtGnn +中的每个原型最相似。最后,我们在各种数据集中评估我们的方法并进行具体的案例研究。广泛的结果表明,Protgnn和Protgnn +可以提供固有的解释性,同时实现与非可解释对方的准确性有关的准确性。
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Molecular representation learning is crucial for the problem of molecular property prediction, where graph neural networks (GNNs) serve as an effective solution due to their structure modeling capabilities. Since labeled data is often scarce and expensive to obtain, it is a great challenge for GNNs to generalize in the extensive molecular space. Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs. It uses self-supervised information to pre-train the GNN, and then performs fine-tuning to optimize the downstream task with just a few labels. However, pre-training does not always yield statistically significant improvement, especially for self-supervised learning with random structural masking. In fact, the molecular structure is characterized by motif subgraphs, which are frequently occurring and influence molecular properties. To leverage the task-related motifs, we propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT). MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. The prompt effectively augments the molecular graph with meaningful motifs in the continuous representation space; this provides more structural patterns to aid the downstream classifier in identifying molecular properties. Extensive experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction, with or without a few fine-tuning steps.
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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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图神经网络的自我监督学习(SSL)正在成为利用未标记数据的有前途的方式。当前,大多数方法基于从图像域改编的对比度学习,该学习需要视图生成和足够数量的负样本。相比之下,现有的预测模型不需要负面抽样,但缺乏关于借口训练任务设计的理论指导。在这项工作中,我们提出了lagraph,这是基于潜在图预测的理论基础的预测SSL框架。 lagraph的学习目标被推导为自我监督的上限,以预测未观察到的潜在图。除了改进的性能外,Lagraph还为包括基于不变性目标的预测模型的最新成功提供了解释。我们提供了比较毛发与不同领域中相关方法的理论分析。我们的实验结果表明,劳拉在性能方面的优势和鲁棒性对于训练样本量减少了图形级别和节点级任务。
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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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