图级表示在各种现实世界中至关重要,例如预测分子的特性。但是实际上,精确的图表注释通常非常昂贵且耗时。为了解决这个问题,图形对比学习构造实例歧视任务,将正面对(同一图的增强对)汇总在一起,并将负面对(不同图的增强对)推开,以进行无监督的表示。但是,由于为了查询,其负面因素是从所有图中均匀抽样的,因此现有方法遭受关键采样偏置问题的损失,即,否定物可能与查询具有相同的语义结构,从而导致性能降解。为了减轻这种采样偏见问题,在本文中,我们提出了一种典型的图形对比度学习(PGCL)方法。具体而言,PGCL通过将语义相似的图形群群归为同一组的群集数据的基础语义结构,并同时鼓励聚类的一致性,以实现同一图的不同增强。然后给出查询,它通过从与查询群集不同的群集中绘制图形进行负采样,从而确保查询及其阴性样本之间的语义差异。此外,对于查询,PGCL根据其原型(集群质心)和查询原型之间的距离进一步重新重新重新重新重新享受其负样本,从而使那些具有中等原型距离的负面因素具有相对较大的重量。事实证明,这种重新加权策略比统一抽样更有效。各种图基准的实验结果证明了我们的PGCL比最新方法的优势。代码可在https://github.com/ha-lins/pgcl上公开获取。
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Graph Contrastive Learning (GCL) has recently drawn much research interest for learning generalizable node representations in a self-supervised manner. In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods. However, nodes sharing similar characteristics may not always be geographically close, which poses a great challenge for unsupervised GCL efforts due to their inherent limitations in capturing such global graph knowledge. In this work, we address their inherent limitations by proposing a simple yet effective framework -- Simple Neural Networks with Structural and Semantic Contrastive Learning} (S^3-CL). Notably, by virtue of the proposed structural and semantic contrastive learning algorithms, even a simple neural network can learn expressive node representations that preserve valuable global structural and semantic patterns. Our experiments demonstrate that the node representations learned by S^3-CL achieve superior performance on different downstream tasks compared with the state-of-the-art unsupervised GCL methods. Implementation and more experimental details are publicly available at \url{https://github.com/kaize0409/S-3-CL.}
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无监督的图形表示学习是图形数据的非琐碎主题。在结构化数据的无监督代表学习中对比学习和自我监督学习的成功激发了图表上的类似尝试。使用对比损耗的当前无监督的图形表示学习和预培训主要基于手工增强图数据之间的对比度。但是,由于不可预测的不变性,图数据增强仍然没有很好地探索。在本文中,我们提出了一种新颖的协作图形神经网络对比学习框架(CGCL),它使用多个图形编码器来观察图形。不同视图观察的特征充当了图形编码器之间对比学习的图表增强,避免了任何扰动以保证不变性。 CGCL能够处理图形级和节点级表示学习。广泛的实验表明CGCL在无监督的图表表示学习中的优势以及图形表示学习的手工数据增强组合的非必要性。
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Generalizable, transferrable, and robust representation learning on graph-structured data remains a challenge for current graph neural networks (GNNs). Unlike what has been developed for convolutional neural networks (CNNs) for image data, self-supervised learning and pre-training are less explored for GNNs. In this paper, we propose a graph contrastive learning (GraphCL) framework for learning unsupervised representations of graph data. We first design four types of graph augmentations to incorporate various priors. We then systematically study the impact of various combinations of graph augmentations on multiple datasets, in four different settings: semi-supervised, unsupervised, and transfer learning as well as adversarial attacks. The results show that, even without tuning augmentation extents nor using sophisticated GNN architectures, our GraphCL framework can produce graph representations of similar or better generalizability, transferrability, and robustness compared to state-of-the-art methods. We also investigate the impact of parameterized graph augmentation extents and patterns, and observe further performance gains in preliminary experiments. Our codes are available at: https://github.com/Shen-Lab/GraphCL.
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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.
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随着各个领域的深度学习的巨大成功,图形神经网络(GNNS)也成为图形分类的主要方法。通过全局读出操作,只会聚合所有节点(或节点群集)表示,现有的GNN分类器获得输入图的图级表示,并使用表示来预测其类标签。但是,这种全局聚合不考虑每个节点的结构信息,这导致全局结构的信息丢失。特别地,它通过对所有节点表示来强制执行分类器的相同权重参数来限制辨别力;在实践中,他们中的每一个都有助于不同于其结构语义的目标类别。在这项工作中,我们提出了结构性语义读数(SSREAD)来总结位置级节点表示,这允许为分类模拟特定位置的权重参数,以及有效地捕获与全局结构相关的图形语义。给定输入图,SSREAD旨在通过使用其节点与结构原型之间的语义对齐来识别结构上有意义的位置,该结构原型编码每个位置的原型特征。结构原型经过优化,以最小化所有训练图的对准成本,而其他GNN参数训练以预测类标签。我们的实验结果表明,SSREAD显着提高了GNN分类器的分类性能和可解释性,同时兼容各种聚合函数,GNN架构和学习框架。
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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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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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在异质图上的自我监督学习(尤其是对比度学习)方法可以有效地摆脱对监督数据的依赖。同时,大多数现有的表示学习方法将异质图嵌入到欧几里得或双曲线的单个几何空间中。这种单个几何视图通常不足以观察由于其丰富的语义和复杂结构而观察到异质图的完整图片。在这些观察结果下,本文提出了一种新型的自我监督学习方法,称为几何对比度学习(GCL),以更好地表示监督数据是不可用时的异质图。 GCL同时观察了从欧几里得和双曲线观点的异质图,旨在强烈合并建模丰富的语义和复杂结构的能力,这有望为下游任务带来更多好处。 GCL通过在局部局部和局部全球语义水平上对比表示两种几何视图之间的相互信息。在四个基准数据集上进行的广泛实验表明,在三个任务上,所提出的方法在包括节点分类,节点群集和相似性搜索在内的三个任务上都超过了强基础,包括无监督的方法和监督方法。
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Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedly change the graph characteristics, and choosing the optimal perturbing ratio for each dataset requires onerous manual tuning. In this paper, we introduce Implicit Graph Contrastive Learning (iGCL), which utilizes augmentations in the latent space learned from a Variational Graph Auto-Encoder by reconstructing graph topological structure. Importantly, instead of explicitly sampling augmentations from latent distributions, we further propose an upper bound for the expected contrastive loss to improve the efficiency of our learning algorithm. Thus, graph semantics can be preserved within the augmentations in an intelligent way without arbitrary manual design or prior human knowledge. Experimental results on both graph-level and node-level tasks show that the proposed method achieves state-of-the-art performance compared to other benchmarks, where ablation studies in the end demonstrate the effectiveness of modules in iGCL.
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对比学习已被广​​泛应用于图形表示学习,其中观测发生器在产生有效的对比样本方面发挥着重要作用。大多数现有的对比学习方法采用预定义的视图生成方法,例如节点滴或边缘扰动,这通常不能适应输入数据或保持原始语义结构。为了解决这个问题,我们提出了一份名为自动化图形对比学习(AutoGCL)的小说框架。具体而言,AutoGCL采用一组由自动增强策略协调的一组学习图形视图生成器,其中每个图形视图生成器都会学习输入调节的图形的概率分布。虽然AutoGCL中的图形视图发生器在生成每个对比样本中保留原始图的最代表性结构,但自动增强学会在整个对比学习程序中介绍适当的增强差异的政策。此外,AutoGCL采用联合培训策略,以培训学习的视图发生器,图形编码器和分类器以端到端的方式,导致拓扑异质性,在产生对比样本时的语义相似性。关于半监督学习,无监督学习和转移学习的广泛实验展示了我们在图形对比学习中的最先进的自动支持者框架的优越性。此外,可视化结果进一步证实,与现有的视图生成方法相比,可学习的视图发生器可以提供更紧凑和语义有意义的对比样本。
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随着对比学习的兴起,无人监督的图形表示学习最近一直蓬勃发展,甚至超过了一些机器学习任务中的监督对应物。图表表示的大多数对比模型学习侧重于最大化本地和全局嵌入之间的互信息,或主要取决于节点级别的对比嵌入。然而,它们仍然不足以全面探索网络拓扑的本地和全球视图。虽然前者认为本地全球关系,但其粗略的全球信息导致本地和全球观点之间的思考。后者注重节点级别对齐,以便全局视图的作用出现不起眼。为避免落入这两个极端情况,我们通过对比群集分配来提出一种新颖的无监督图形表示模型,称为GCCA。通过组合聚类算法和对比学习,它有动力综合利用本地和全球信息。这不仅促进了对比效果,而且还提供了更高质量的图形信息。同时,GCCA进一步挖掘群集级信息,这使得它能够了解除了图形拓扑之外的节点之间的难以捉摸的关联。具体地,我们首先使用不同的图形增强策略生成两个增强的图形,然后使用聚类算法分别获取其群集分配和原型。所提出的GCCA进一步强制不同增强图中的相同节点来通过最小化交叉熵损失来互相识别它们的群集分配。为了展示其有效性,我们将在三个不同的下游任务中与最先进的模型进行比较。实验结果表明,GCCA在大多数任务中具有强大的竞争力。
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Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.
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图形存在于许多现实世界中的应用中,例如财务欺诈检测,商业建议和社交网络分析。但是,鉴于图形注释或标记的高成本,我们面临严重的图形标签 - 刻度问题,即,图可能具有一些标记的节点。这样一个问题的一个例子是所谓的\ textit {少数弹性节点分类}。该问题的主要方法均依靠\ textit {情节元学习}。在这项工作中,我们通过提出一个基本问题来挑战现状,元学习是否是对几个弹性节点分类任务的必要条件。我们在标准的几杆节点分类设置下提出了一个新的简单框架,作为学习有效图形编码器的元学习的替代方法。该框架由有监督的图形对比学习以及新颖的数据增强,子图编码和图形上的多尺度对比度组成。在三个基准数据集(Corafull,Reddit,OGBN)上进行的广泛实验表明,新框架显着胜过基于最先进的元学习方法。
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Inspired by the success of contrastive learning (CL) in computer vision and natural language processing, graph contrastive learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant stage. For example, it is unclear how to augment the HINs without substantially altering the underlying semantics, and how to design the contrastive objective to fully capture the rich semantics. Moreover, early investigations demonstrate that CL suffers from sampling bias, whereas conventional debiasing techniques are empirically shown to be inadequate for GCL. How to mitigate the sampling bias for heterogeneous GCL is another important problem. To address the aforementioned challenges, we propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In particular, we use metapaths as the augmentation to generate multiple subgraphs as multi-views, and propose a contrastive objective to maximize the mutual information between any pairs of metapath-induced views. To alleviate the sampling bias, we further propose a positive sampling strategy to explicitly select positives for each node via jointly considering semantic and structural information preserved on each metapath view. Extensive experiments demonstrate HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets.
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We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to more than two or contrasting multi-scale encodings do not improve performance, and the best performance is achieved by contrasting encodings from first-order neighbors and a graph diffusion. We achieve new state-ofthe-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol. For example, on Cora (node) and Reddit-Binary (graph) classification benchmarks, we achieve 86.8% and 84.5% accuracy, which are 5.5% and 2.4% relative improvements over previous state-of-the-art. When compared to supervised baselines, our approach outperforms them in 4 out of 8 benchmarks.
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图表表示学习(GRL)对于图形结构数据分析至关重要。然而,大多数现有的图形神经网络(GNNS)严重依赖于标签信息,这通常是在现实世界中获得的昂贵。现有无监督的GRL方法遭受某些限制,例如对单调对比和可扩展性有限的沉重依赖。为了克服上述问题,鉴于最近的图表对比学习的进步,我们通过曲线图介绍了一种新颖的自我监控图形表示学习算法,即通过利用所提出的调整变焦方案来学习节点表示来学习节点表示。具体地,该机制使G-Zoom能够从多个尺度的图表中探索和提取自我监督信号:MICRO(即,节点级别),MESO(即,邻域级)和宏(即,子图级) 。首先,我们通过两个不同的图形增强生成输入图的两个增强视图。然后,我们逐渐地从节点,邻近逐渐为上述三个尺度建立三种不同的对比度,在那里我们最大限度地提高了横跨尺度的图形表示之间的协议。虽然我们可以从微距和宏观视角上从给定图中提取有价值的线索,但是邻域级对比度基于我们的调整后的缩放方案提供了可自定义选项的能力,以便手动选择位于微观和介于微观之间的最佳视点宏观透视更好地理解图数据。此外,为了使我们的模型可扩展到大图,我们采用了并行图形扩散方法来从图形尺寸下解耦模型训练。我们对现实世界数据集进行了广泛的实验,结果表明,我们所提出的模型始终始终优于最先进的方法。
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图形对比学习(GCL)已成为学习图形无监督表示的有效工具。关键思想是通过数据扩展最大化每个图的两个增强视图之间的一致性。现有的GCL模型主要集中在给定情况下的所有图表上应用\ textit {相同的增强策略}。但是,实际图通常不是单态,而是各种本质的抽象。即使在相同的情况下(例如,大分子和在线社区),不同的图形可能需要各种增强来执行有效的GCL。因此,盲目地增强所有图表而不考虑其个人特征可能会破坏GCL艺术的表现。 {a} u Mentigation(GPA),通过允许每个图选择自己的合适的增强操作来推进常规GCL。本质上,GPA根据其拓扑属性和节点属性通过可学习的增强选择器为每个图定制了量身定制的增强策略,该策略是插件模块,可以通过端到端的下游GCL型号有效地训练。来自不同类型和域的11个基准图的广泛实验证明了GPA与最先进的竞争对手的优势。此外,通过可视化不同类型的数据集中学习的增强分布,我们表明GPA可以有效地识别最合适的数据集每个图的增强基于其特征。
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尽管有关超图的机器学习吸引了很大的关注,但大多数作品都集中在(半)监督的学习上,这可能会导致繁重的标签成本和不良的概括。最近,对比学习已成为一种成功的无监督表示学习方法。尽管其他领域中对比度学习的发展繁荣,但对超图的对比学习仍然很少探索。在本文中,我们提出了Tricon(三个方向对比度学习),这是对超图的对比度学习的一般框架。它的主要思想是三个方向对比度,具体来说,它旨在在两个增强视图中最大化同一节点之间的协议(a),(b)在同一节点之间以及(c)之间,每个组之间的成员及其成员之间的协议(b) 。加上简单但令人惊讶的有效数据增强和负抽样方案,这三种形式的对比使Tricon能够在节点嵌入中捕获显微镜和介观结构信息。我们使用13种基线方法,5个数据集和两个任务进行了广泛的实验,这证明了Tricon的有效性,最明显的是,Tricon始终优于无监督的竞争对手,而且(半)受监督的竞争对手,大多数是由大量的节点分类的大量差额。
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对比学习在图表学习领域表现出了巨大的希望。通过手动构建正/负样本,大多数图对比度学习方法依赖于基于矢量内部产品的相似性度量标准来区分图形表示样品。但是,手工制作的样品构建(例如,图表的节点或边缘的扰动)可能无法有效捕获图形的固有局部结构。同样,基于矢量内部产品的相似性度量标准无法完全利用图形的局部结构来表征图差。为此,在本文中,我们提出了一种基于自适应子图生成的新型对比度学习框架,以实现有效且强大的自我监督图表示学习,并且最佳传输距离被用作子绘图之间的相似性度量。它的目的是通过捕获图的固有结构来生成对比样品,并根据子图的特征和结构同时区分样品。具体而言,对于每个中心节点,通过自适应学习关系权重与相应邻域的节点,我们首先开发一个网络来生成插值子图。然后,我们分别构建来自相同和不同节点的子图的正和负对。最后,我们采用两种类型的最佳运输距离(即Wasserstein距离和Gromov-Wasserstein距离)来构建结构化的对比损失。基准数据集上的广泛节点分类实验验证了我们的图形对比学习方法的有效性。
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