图神经网络（GNN）在图形上学习节点表示方面表现出很大的力量。但是，他们可能会从训练数据中继承历史偏见，从而导致预测的歧视性偏见。尽管某些工作已经开发出公平的GNN，但其中大多数直接从非图形域借用了公平代表性学习技术，而没有考虑GNN中特征传播引起的敏感属性泄漏的潜在问题。但是，我们从经验上观察到，特征传播可能会改变以前无害特征与敏感特征的相关性。这可以看作是敏感信息的泄漏，可以进一步加剧预测中的歧视。因此，我们根据特征相关性设计了两个特征掩盖策略，以突出考虑特征传播和相关性变化在减轻歧视中的重要性。通过我们的分析，我们提出了公平视图图神经网络（FAIRVGNN），以通过自动识别和掩盖敏感的相关特征来生成特征的公平视图，以考虑特征传播后的相关变化。鉴于博学的公平视图，我们适应编码器的夹紧权重，以避免使用敏感相关的功能。现实世界数据集的实验表明，Fairvgnn在模型实用程序和公平性之间取得了更好的权衡。我们的代码可在https://github.com/yuwvandy/fairvgnn上公开获取。

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。

While machine learning models have achieved unprecedented success in real-world applications, they might make biased/unfair decisions for specific demographic groups and hence result in discriminative outcomes. Although research efforts have been devoted to measuring and mitigating bias, they mainly study bias from the result-oriented perspective while neglecting the bias encoded in the decision-making procedure. This results in their inability to capture procedure-oriented bias, which therefore limits the ability to have a fully debiasing method. Fortunately, with the rapid development of explainable machine learning, explanations for predictions are now available to gain insights into the procedure. In this work, we bridge the gap between fairness and explainability by presenting a novel perspective of procedure-oriented fairness based on explanations. We identify the procedure-based bias by measuring the gap of explanation quality between different groups with Ratio-based and Value-based Explanation Fairness. The new metrics further motivate us to design an optimization objective to mitigate the procedure-based bias where we observe that it will also mitigate bias from the prediction. Based on our designed optimization objective, we propose a Comprehensive Fairness Algorithm (CFA), which simultaneously fulfills multiple objectives - improving traditional fairness, satisfying explanation fairness, and maintaining the utility performance. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed CFA and highlight the importance of considering fairness from the explainability perspective. Our code is publicly available at https://github.com/YuyingZhao/FairExplanations-CFA .

图形神经网络（GNNS）在学习图表表示中取得了前所未有的成功，以识别图形的分类标签。然而，GNN的大多数现有图形分类问题遵循平衡数据拆分协议，这与许多真实情景中的许多实际方案都有比其他类别更少的标签。在这种不平衡情况下直接培训GNN可能导致少数群体类别中的图形的无色表达，并损害下游分类的整体性能，这意味着开发有效GNN处理不平衡图分类的重要性。现有方法是针对非图形结构数据量身定制的，或专为不平衡节点分类而设计，而少数关注不平衡图分类。为此，我们介绍了一个新颖的框架，图形图形 - 图形神经网络（G $ ^ 2 $ GNN），通过从邻近图和本地从图形本身来源地通过全局导出额外的监督来减轻图形不平衡问题。在全球范围内，我们基于内核相似性构建图表（GOG）的图表，并执行GOG传播以聚合相邻图形表示，其最初通过通过GNN编码器汇集的节点级传播而获得。在本地，我们通过掩模节点或丢弃边缘采用拓扑增强，以改善辨别说明书测试图的拓扑结构中的模型概括性。在七个基准数据集中进行的广泛图形分类实验证明了我们提出的G $ ^ $ ^ 2 $ GNN优于F1-Macro和F1-Micro Scores的大约5 \％的大量基线。 G $ ^ 2 $ GNN的实现可用于\ href {https://github.com/yuwvandy/g2gnn} {https://github.com/yuwvandy/g2gnn}。

图形神经网络（GNNS）已被证明是在预测建模任务中的Excel，其中底层数据是图形。然而，由于GNN广泛用于人以人为本的应用，因此出现了公平性问题。虽然边缘删除是用于促进GNNS中公平性的常用方法，但是当数据本质上缺少公平连接时，它就无法考虑。在这项工作中，我们考虑未删除的边缘添加方法，促进公平。我们提出了两个模型 - 不可知的算法来执行边缘编辑：蛮力方法和连续近似方法，公平。Fairedit通过利用公平损失的梯度信息来执行有效的边缘编辑，以找到改善公平性的边缘。我们发现Fairedit优于许多数据集和GNN方法的标准培训，同时表现了许多最先进的方法，展示了公平的能力，以改善许多领域和模型的公平性。

Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation on the input graph to obtain two graph views and maximize the agreement of representations in the two views. Despite the prosperous development of graph CL methods, the design of graph augmentation schemes-a crucial component in CL-remains rarely explored. We argue that the data augmentation schemes should preserve intrinsic structures and attributes of graphs, which will force the model to learn representations that are insensitive to perturbation on unimportant nodes and edges. However, most existing methods adopt uniform data augmentation schemes, like uniformly dropping edges and uniformly shuffling features, leading to suboptimal performance. In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. Specifically, on the topology level, we design augmentation schemes based on node centrality measures to highlight important connective structures. On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information. We perform extensive experiments of node classification on a variety of real-world datasets. Experimental results demonstrate that our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts, which validates the effectiveness of the proposed contrastive framework with adaptive augmentation. CCS CONCEPTS• Computing methodologies → Unsupervised learning; Neural networks; Learning latent representations.

公平机器学习旨在减轻模型预测的偏见，这对于关于诸如种族和性别等敏感属性的某些群体的偏见。在许多现有的公平概念中，反事实公平通过比较来自原始数据和反事实的预测来衡量因因果角度来源的模型公平。在反事实上，该个人的敏感属性值已被修改。最近，少数作品将反事实公平扩展到图数据，但大多数忽略了可能导致偏差的以下事实：1）每个节点邻居的敏感属性可能会影响预测w.r.t.这个节点; 2）敏感属性可能会导致其他特征和图形结构。为了解决这些问题，在本文中，我们提出了一种新颖的公平概念 - 图形反应性公平，这考虑了上述事实领导的偏差。要学习对图形反事实公平的节点表示，我们提出了一种基于反事实数据增强的新颖框架。在此框架中，我们生成对应于每个节点和邻居敏感属性的扰动的反应性。然后，我们通过最大限度地减少从原始图表中学到的表示与每个节点的反事实之间的差异来执行公平性。合成和真实图的实验表明，我们的框架优于图形反事实公平性的最先进的基线，并且还实现了可比的预测性能。

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

近年来，图形神经网络（GNNS）在许多现实世界中的应用（例如建议和药物发现）中取得了巨大的成功。尽管取得了成功，但已将过度厚度确定为限制GNN绩效的关键问题之一。这表明由于堆叠聚合器，学到的节点表示是无法区分的。在本文中，我们提出了一种新的观点，以研究深度GNN的性能降低，即特征过度相关。通过有关此问题的经验和理论研究，我们证明了更深层次的GNN中的特征过度相关的存在，并揭示了导致该问题的潜在原因。为了减少功能相关性，我们提出了一个通用框架，可以鼓励GNN编码较少的冗余信息。广泛的实验表明，Decorr可以帮助实现更深入的GNN，并与现有的技术相辅相成。

Learning fair graph representations for downstream applications is becoming increasingly important, but existing work has mostly focused on improving fairness at the global level by either modifying the graph structure or objective function without taking into account the local neighborhood of a node. In this work, we formally introduce the notion of neighborhood fairness and develop a computational framework for learning such locally fair embeddings. We argue that the notion of neighborhood fairness is more appropriate since GNN-based models operate at the local neighborhood level of a node. Our neighborhood fairness framework has two main components that are flexible for learning fair graph representations from arbitrary data: the first aims to construct fair neighborhoods for any arbitrary node in a graph and the second enables adaption of these fair neighborhoods to better capture certain application or data-dependent constraints, such as allowing neighborhoods to be more biased towards certain attributes or neighbors in the graph.Furthermore, while link prediction has been extensively studied, we are the first to investigate the graph representation learning task of fair link classification. We demonstrate the effectiveness of the proposed neighborhood fairness framework for a variety of graph machine learning tasks including fair link prediction, link classification, and learning fair graph embeddings. Notably, our approach achieves not only better fairness but also increases the accuracy in the majority of cases across a wide variety of graphs, problem settings, and metrics.

Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also face some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate augmented data into models by randomly masking parts of the input. However, some open problems of random dropping on GNNs remain to be solved. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, augmented data introduced to GNNs causes the incomplete coverage of parameters and unstable training process. Third, there is no theoretical analysis on the effectiveness of random dropping methods on GNNs. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the propagated messages during the message-passing process. More importantly, we find that DropMessage provides a unified framework for most existing random dropping methods, based on which we give theoretical analysis of their effectiveness. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, enabling it become a theoretical upper bound of other methods. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has the advantages of both effectiveness and generalization, and can significantly alleviate the problems mentioned above.

Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner. Existing methods construct the contrastive samples by adding perturbations to the graph structure or node attributes. Although impressive results are achieved, it is rather blind to the wealth of prior information assumed: with the increase of the perturbation degree applied on the original graph, 1) the similarity between the original graph and the generated augmented graph gradually decreases; 2) the discrimination between all nodes within each augmented view gradually increases. In this paper, we argue that both such prior information can be incorporated (differently) into the contrastive learning paradigm following our general ranking framework. In particular, we first interpret CL as a special case of learning to rank (L2R), which inspires us to leverage the ranking order among positive augmented views. Meanwhile, we introduce a self-ranking paradigm to ensure that the discriminative information among different nodes can be maintained and also be less altered to the perturbations of different degrees. Experiment results on various benchmark datasets verify the effectiveness of our algorithm compared with the supervised and unsupervised models.

异质图卷积网络在解决异质网络数据的各种网络分析任务方面已广受欢迎，从链接预测到节点分类。但是，大多数现有作品都忽略了多型节点之间的多重网络的关系异质性，而在元路径中，元素嵌入中关系的重要性不同，这几乎无法捕获不同关系跨不同关系的异质结构信号。为了应对这一挑战，这项工作提出了用于异质网络嵌入的多重异质图卷积网络（MHGCN）。我们的MHGCN可以通过多层卷积聚合自动学习多重异质网络中不同长度的有用的异质元路径相互作用。此外，我们有效地将多相关结构信号和属性语义集成到学习的节点嵌入中，并具有无监督和精选的学习范式。在具有各种网络分析任务的五个现实世界数据集上进行的广泛实验表明，根据所有评估指标，MHGCN与最先进的嵌入基线的优势。

数据增强已广泛用于图像数据和语言数据，但仍然探索图形神经网络（GNN）。现有方法专注于从全局视角增强图表数据，并大大属于两个类型：具有特征噪声注入的结构操纵和对抗训练。但是，最近的图表数据增强方法忽略了GNNS“消息传递机制的本地信息的重要性。在这项工作中，我们介绍了本地增强，这通过其子图结构增强了节点表示的局部。具体而言，我们将数据增强模拟为特征生成过程。鉴于节点的功能，我们的本地增强方法了解其邻居功能的条件分布，并生成更多邻居功能，以提高下游任务的性能。基于本地增强，我们进一步设计了一个新颖的框架：La-GNN，可以以即插即用的方式应用于任何GNN模型。广泛的实验和分析表明，局部增强一致地对各种基准的各种GNN架构始终如一地产生性能改进。

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.

图表学习目的旨在将节点内容与图形结构集成以学习节点/图表示。然而，发现许多现有的图形学习方法在具有高异性级别的数据上不能很好地工作，这是不同类标签之间很大比例的边缘。解决这个问题的最新努力集中在改善消息传递机制上。但是，尚不清楚异质性是否确实会损害图神经网络（GNNS）的性能。关键是要展现一个节点与其直接邻居之间的关系，例如它们是异性还是同质性？从这个角度来看，我们在这里研究了杂质表示在披露连接节点之间的关系之前/之后的杂音表示的作用。特别是，我们提出了一个端到端框架，该框架既学习边缘的类型（即异性/同质性），并利用边缘类型的信息来提高图形神经网络的表现力。我们以两种不同的方式实施此框架。具体而言，为了避免通过异质边缘传递的消息，我们可以通过删除边缘分类器鉴定的异性边缘来优化图形结构。另外，可以利用有关异性邻居的存在的信息进行特征学习，因此，设计了一种混合消息传递方法来汇总同质性邻居，并根据边缘分类使异性邻居多样化。广泛的实验表明，在整个同质级别的多个数据集上，通过在多个数据集上提出的框架对GNN的绩效提高了显着提高。

因子化机器（FM）是在处理高维稀疏数据时建模成对（二阶）特征交互的普遍存在方法。然而，一方面，FM无法捕获患有组合扩展的高阶特征相互作用，另一方面，考虑每对特征之间的相互作用可能引入噪声和降低预测精度。为了解决问题，我们通过在图形结构中自然表示特征来提出一种新颖的方法图形因子分子机器（GraphFM）。特别地，设计了一种新颖的机制来选择有益特征相互作用，并将它们装配为特征之间的边缘。然后我们所提出的模型将FM的交互功能集成到图形神经网络（GNN）的特征聚合策略中，可以通过堆叠图层模拟图形结构特征上的任意顺序特征交互。关于若干现实世界数据集的实验结果表明了我们提出的方法的合理性和有效性。

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。

图表表示学习已经成为许多情景中的无处不在的组成部分，从社会网络分析到智能电网的能量预测。在几个应用程序中，确保关于某些受保护属性的节点（或图形）表示的公平对其正确部署至关重要。然而，图表深度学习的公平仍然在探索，很少有解决方案。特别地，在若干真实世界图（即同声源性）上相似节点对簇的趋势可以显着恶化这些程序的公平性。在本文中，我们提出了一种新颖的偏见边缘辍学算法（Fairdrop）来反击精神剧并改善图形表示学习中的公平性。 Fairdrop可以在许多现有算法上轻松插入，具有高效，适应性，并且可以与其他公平诱导的解决方案结合。在描述了一般算法之后，我们在两个基准任务中展示其应用，具体地，作为用于生产节点嵌入的随机步道模型，以及用于链路预测的图形卷积网络。我们证明，所提出的算法可以成功地改善所有型号的公平，直到精度小或可忽略的降低，并与现有的最先进的解决方案相比。在一个消融研究中，我们证明我们的算法可以灵活地在偏置公平性和无偏见的边缘辍学之间插入。此外，为了更好地评估增益，我们提出了一种新的二元组定义，以测量与基于组的公平度量配对时的链路预测任务的偏差。特别是，我们扩展了用于测量节点嵌入的偏差的指标，以考虑图形结构。

由于事后解释越来越多地用于了解图神经网络（GNN）的行为，因此评估GNN解释的质量和可靠性至关重要。但是，评估GNN解释的质量是具有挑战性的，因为现有的图形数据集对给定任务没有或不可靠的基础真相解释。在这里，我们介绍了一个合成图数据生成器ShapeGgen，该生成可以生成各种基准数据集（例如，不同的图形大小，度分布，同粒细胞与异性图）以及伴随着地面真相解释。此外，生成各种合成数据集和相应的基础真相解释的灵活性使我们能够模仿各种现实世界应用程序生成的数据。我们将ShapeGgen和几个现实图形数据集包括在开源图形图库GraphXai中。除了带有基础真相说明的合成和现实图形数据集外，GraphXAI还提供数据加载程序，数据处理功能，可视化器，GNN模型实现和评估指标，以基准基准GNN解释性方法的性能。