Graph machine learning has been extensively studied in both academia and industry. Although booming with a vast number of emerging methods and techniques, most of the literature is built on the in-distribution hypothesis, i.e., testing and training graph data are identically distributed. However, this in-distribution hypothesis can hardly be satisfied in many real-world graph scenarios where the model performance substantially degrades when there exist distribution shifts between testing and training graph data. To solve this critical problem, out-of-distribution (OOD) generalization on graphs, which goes beyond the in-distribution hypothesis, has made great progress and attracted ever-increasing attention from the research community. In this paper, we comprehensively survey OOD generalization on graphs and present a detailed review of recent advances in this area. First, we provide a formal problem definition of OOD generalization on graphs. Second, we categorize existing methods into three classes from conceptually different perspectives, i.e., data, model, and learning strategy, based on their positions in the graph machine learning pipeline, followed by detailed discussions for each category. We also review the theories related to OOD generalization on graphs and introduce the commonly used graph datasets for thorough evaluations. Finally, we share our insights on future research directions. This paper is the first systematic and comprehensive review of OOD generalization on graphs, to the best of our knowledge.
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Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which suggests that the train and test data are sampled from the same distribution. However, this assumption seldom holds in the real world due to distribution shifts. As a result models that rely on this assumption exhibit poor generalization capabilities. Over the recent years, dedicated efforts have been made to improve the generalization capabilities of these models collectively known as -- \textit{domain generalization methods}. The primary idea behind these methods is to identify stable features or mechanisms that remain invariant across the different distributions. Many generalization approaches employ causal theories to describe invariance since causality and invariance are inextricably intertwined. However, current surveys deal with the causality-aware domain generalization methods on a very high-level. Furthermore, we argue that it is possible to categorize the methods based on how causality is leveraged in that method and in which part of the model pipeline is it used. To this end, we categorize the causal domain generalization methods into three categories, namely, (i) Invariance via Causal Data Augmentation methods which are applied during the data pre-processing stage, (ii) Invariance via Causal representation learning methods that are utilized during the representation learning stage, and (iii) Invariance via Transferring Causal mechanisms methods that are applied during the classification stage of the pipeline. Furthermore, this survey includes in-depth insights into benchmark datasets and code repositories for domain generalization methods. We conclude the survey with insights and discussions on future directions.
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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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建议图表神经网络(GNNS)在不考虑训练和测试图之间的不可知分布的情况下,诱导GNN的泛化能力退化在分布外(OOD)设置。这种退化的根本原因是大多数GNN是基于I.I.D假设开发的。在这种设置中,GNN倾向于利用在培训中存在的微妙统计相关性用于预测,即使它是杂散的相关性。然而,这种杂散的相关性可能在测试环境中改变,导致GNN的失败。因此,消除了杂散相关的影响对于稳定的GNN来说是至关重要的。为此,我们提出了一个普遍的因果代表框架,称为稳定凝球。主要思想是首先从图数据中提取高级表示,并诉诸因因果推理的显着能力,以帮助模型摆脱虚假相关性。特别是,我们利用图形池化层以提取基于子图的表示作为高级表示。此外,我们提出了一种因果变量区别,以纠正偏置训练分布。因此,GNN将更多地集中在稳定的相关性上。对合成和现实世界ood图数据集的广泛实验良好地验证了所提出的框架的有效性,灵活性和可解释性。
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图形神经网络是一种强大的深度学习工具,用于建模图形结构化数据,在众多图形学习任务上表现出了出色的性能。为了解决深图学习中的数据噪声和数据稀缺性问题,最近有关图形数据的研究已加剧。但是,常规数据增强方法几乎无法处理具有多模式性的非欧几里得空间中定义的图形结构化数据。在这项调查中,我们正式提出了图数据扩展的问题,并进一步审查了代表性技术及其在不同深度学习问题中的应用。具体而言,我们首先提出了图形数据扩展技术的分类法,然后通过根据增强信息方式对相关工作进行分类,从而提供结构化的审查。此外,我们总结了以数据为中心的深图学习中两个代表性问题中图数据扩展的应用:(1)可靠的图形学习,重点是增强输入图的实用性以及通过图数据增强的模型容量; (2)低资源图学习,其针对通过图数据扩大标记的训练数据量表的目标。对于每个问题,我们还提供层次结构问题分类法,并审查与图数据增强相关的现有文献。最后,我们指出了有希望的研究方向和未来研究的挑战。
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图表神经网络(GNNS)在测试和训练图数据来自相同分布时取得了令人印象深刻的性能。然而,现有的GNN缺乏分发的泛化能力,使得它们的性能在测试和训练图数据之间存在分布时显着降低。为了解决这个问题,在这项工作中,我们提出了一个用于在具有训练图的不同分布的看不见的分布的看不见的令人满意的令人满意的令人满意的通用图形神经网络(OOD-GNN)。我们所提出的OOD-GNN采用新颖的非线性图形表示去序方法,利用随机傅里叶特征,这鼓励模型通过迭代优化样本图权重和图形编码器来消除相关和无关的图表表示之间的统计依赖性。我们进一步设计了一个全局重量估计器,以学习训练图的权重,使得图形表示中的变量被迫独立。学习权重有助于图形编码器摆脱虚假相关性,并且反过来,更集中学习鉴别图形表示与地面真理标签之间的真实连接。我们进行广泛的实验,以验证两个合成和12个现实世界数据集的分发外概括能力,分配换档。结果表明,我们所提出的OOD-GNN显着优于最先进的基线。
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学习强大的表示是图形神经网络(GNN)的一个中心主题。它需要从输入图中炼制关键信息,而不是琐碎的模式,以丰富表示。为此,图表注意力和汇集方法占上风。他们主要遵循“学会参加”的范式。它最大限度地提高了上述子图和地面真理标签之间的相互信息。然而,这种训练范例易于捕获微级子图和标签之间的虚假相关性。这种杂散的相关性对分布(ID)测试评估有益,但在分布外(OOD)测试数据中引起差的概括。在这项工作中,我们从因果角度重新审视GNN建模。在我们的因果假设之上,琐碎的信息是关键信息和标签之间的混淆,它在它们之间打开了一个后门路径,使它们保持虚拟相关。因此,我们提出了一个新的解压缩训练范式(DTP),更好地减轻了批评信息的混淆效果并锁存,以提高表示和泛化能力。具体而言,我们采用注意模块解开关键的子图和微不足道的子图。然后我们使每个关键的子图相当与不同的琐碎子图相互作用,以实现稳定的预测。它允许GNN捕获一个更可靠的子图,其与标签的关系跨越不同的分布。我们对综合和现实世界数据集进行了广泛的实验,以证明有效性。
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关于图表的深度学习最近吸引了重要的兴趣。然而,大多数作品都侧重于(半)监督学习,导致缺点包括重标签依赖,普遍性差和弱势稳健性。为了解决这些问题,通过良好设计的借口任务在不依赖于手动标签的情况下提取信息知识的自我监督学习(SSL)已成为图形数据的有希望和趋势的学习范例。与计算机视觉和自然语言处理等其他域的SSL不同,图表上的SSL具有独家背景,设计理念和分类。在图表的伞下自我监督学习,我们对采用图表数据采用SSL技术的现有方法及时及全面的审查。我们构建一个统一的框架,数学上正式地规范图表SSL的范例。根据借口任务的目标,我们将这些方法分为四类:基于生成的,基于辅助性的,基于对比的和混合方法。我们进一步描述了曲线图SSL在各种研究领域的应用,并总结了绘图SSL的常用数据集,评估基准,性能比较和开源代码。最后,我们讨论了该研究领域的剩余挑战和潜在的未来方向。
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机器学习系统通常假设训练和测试分布是相同的。为此,关键要求是开发可以概括到未经看不见的分布的模型。领域泛化(DG),即分销概括,近年来引起了越来越令人利益。域概括处理了一个具有挑战性的设置,其中给出了一个或几个不同但相关域,并且目标是学习可以概括到看不见的测试域的模型。多年来,域概括地区已经取得了巨大进展。本文提出了对该地区最近进步的首次审查。首先,我们提供了域泛化的正式定义,并讨论了几个相关领域。然后,我们彻底审查了与域泛化相关的理论,并仔细分析了泛化背后的理论。我们将最近的算法分为三个类:数据操作,表示学习和学习策略,并为每个类别详细介绍几种流行的算法。第三,我们介绍常用的数据集,应用程序和我们的开放源代码库进行公平评估。最后,我们总结了现有文学,并为未来提供了一些潜在的研究主题。
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In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. For promoting the development of this emerging research direction, in this survey, we comprehensively review and summarize the existing graph data augmentation (GDAug) techniques. Specifically, we first summarize a variety of feasible taxonomies, and then classify existing GDAug studies based on fine-grained graph elements. Furthermore, for each type of GDAug technique, we formalize the general definition, discuss the technical details, and give schematic illustration. In addition, we also summarize common performance metrics and specific design metrics for constructing a GDAug evaluation system. Finally, we summarize the applications of GDAug from both data and model levels, as well as future directions.
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Pre-publication draft of a book to be published byMorgan & Claypool publishers. Unedited version released with permission. All relevant copyrights held by the author and publisher extend to this pre-publication draft.
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分数(OOD)学习涉及培训和测试数据遵循不同分布的方案。尽管在机器学习中已经深入研究了一般的OOD问题,但图形OOD只是一个新兴领域。目前,缺少针对图形OOD方法评估的系统基准。在这项工作中,我们旨在为图表开发一个被称为GOOD的OOD基准。我们明确地在协变量和概念变化和设计数据拆分之间进行了区分,以准确反映不同的变化。我们考虑图形和节点预测任务,因为在设计变化时存在关键差异。总体而言,Good包含8个具有14个域选择的数据集。当与协变量,概念和无移位结合使用时,我们获得了42个不同的分裂。我们在7种常见的基线方法上提供了10种随机运行的性能结果。这总共导致294个数据集模型组合。我们的结果表明,分布和OOD设置之间的性能差距很大。我们的结果还阐明了通过不同方法的协变量和概念转移之间的不同性能趋势。我们的良好基准是一个不断增长的项目,并希望随着该地区的发展,数量和种类繁多。可以通过$ \ href {https://github.com/divelab/good/} {\ text {https://github.com/divelab/good/good/}} $访问良好基准。
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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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Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.
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大多数图形神经网络(GNN)通过学习输入图和标签之间的相关性来预测看不见的图的标签。但是,通过对具有严重偏见的训练图进行图形分类调查,我们发现GNN始终倾向于探索伪造的相关性以做出决定,即使因果关系始终存在。这意味着在此类偏见的数据集中接受培训的现有GNN将遭受概括能力差。通过在因果观点中分析此问题,我们发现从偏见图中解开和去偏置因果和偏见的潜在变量对于偏见至关重要。在此鼓舞下,我们提出了一个普遍的分解GNN框架,分别学习因果子结构和偏见子结构。特别是,我们设计了一个参数化的边蒙版生成器,以将输入图明确分为因果和偏置子图。然后,分别由因果/偏见感知损失函数监督的两个GNN模块进行培训,以编码因果关系和偏置子图表中的相应表示。通过分离的表示,我们合成了反事实无偏的训练样本,以进一步脱离因果变量和偏见变量。此外,为了更好地基于严重的偏见问题,我们构建了三个新的图形数据集,这些数据集具有可控的偏置度,并且更容易可视化和解释。实验结果很好地表明,我们的方法比现有基线实现了优越的概括性能。此外,由于学习的边缘面膜,该拟议的模型具有吸引人的解释性和可转让性。代码和数据可在以下网址获得:https://github.com/googlebaba/disc。
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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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组合优化是运营研究和计算机科学领域的一个公认领域。直到最近,它的方法一直集中在孤立地解决问题实例,而忽略了它们通常源于实践中的相关数据分布。但是,近年来,人们对使用机器学习,尤其是图形神经网络(GNN)的兴趣激增,作为组合任务的关键构件,直接作为求解器或通过增强确切的求解器。GNN的电感偏差有效地编码了组合和关系输入,因为它们对排列和对输入稀疏性的意识的不变性。本文介绍了对这个新兴领域的最新主要进步的概念回顾,旨在优化和机器学习研究人员。
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Out-of-distribution (OOD) generalization on graphs is drawing widespread attention. However, existing efforts mainly focus on the OOD issue of correlation shift. While another type, covariate shift, remains largely unexplored but is the focus of this work. From a data generation view, causal features are stable substructures in data, which play key roles in OOD generalization. While their complementary parts, environments, are unstable features that often lead to various distribution shifts. Correlation shift establishes spurious statistical correlations between environments and labels. In contrast, covariate shift means that there exist unseen environmental features in test data. Existing strategies of graph invariant learning and data augmentation suffer from limited environments or unstable causal features, which greatly limits their generalization ability on covariate shift. In view of that, we propose a novel graph augmentation strategy: Adversarial Causal Augmentation (AdvCA), to alleviate the covariate shift. Specifically, it adversarially augments the data to explore diverse distributions of the environments. Meanwhile, it keeps the causal features invariant across diverse environments. It maintains the environmental diversity while ensuring the invariance of the causal features, thereby effectively alleviating the covariate shift. Extensive experimental results with in-depth analyses demonstrate that AdvCA can outperform 14 baselines on synthetic and real-world datasets with various covariate shifts.
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Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.
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Causal learning has attracted much attention in recent years because causality reveals the essential relationship between things and indicates how the world progresses. However, there are many problems and bottlenecks in traditional causal learning methods, such as high-dimensional unstructured variables, combinatorial optimization problems, unknown intervention, unobserved confounders, selection bias and estimation bias. Deep causal learning, that is, causal learning based on deep neural networks, brings new insights for addressing these problems. While many deep learning-based causal discovery and causal inference methods have been proposed, there is a lack of reviews exploring the internal mechanism of deep learning to improve causal learning. In this article, we comprehensively review how deep learning can contribute to causal learning by addressing conventional challenges from three aspects: representation, discovery, and inference. We point out that deep causal learning is important for the theoretical extension and application expansion of causal science and is also an indispensable part of general artificial intelligence. We conclude the article with a summary of open issues and potential directions for future work.
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