基于图的异常检测已被广泛用于检测现实世界应用中的恶意活动。迄今为止,现有的解决此问题的尝试集中在二进制分类制度中的结构特征工程或学习上。在这项工作中,我们建议利用图形对比编码,并提出监督的GCCAD模型,以将异常节点与正常节点的距离与全球环境(例如所有节点的平均值)相比。为了使用稀缺标签处理场景,我们通过设计用于生成合成节点标签的图形损坏策略,进一步使GCCAD成为一个自制的框架。为了实现对比目标,我们设计了一个图形神经网络编码器,该编码器可以在消息传递过程中推断并进一步删除可疑链接,并了解输入图的全局上下文。我们在四个公共数据集上进行了广泛的实验,表明1)GCCAD显着且始终如一地超过各种高级基线,2)其自我监督版本没有微调可以通过其完全监督的版本来实现可比性的性能。
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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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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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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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Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8x faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.
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Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.
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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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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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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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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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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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近年来,图形神经网络(GNNS)已实现了节点分类的最新性能。但是,大多数现有的GNN会遭受图形不平衡问题。在许多实际情况下,节点类都是不平衡的,其中一些多数类构成了图的大部分部分。 GNN中的消息传播机制将进一步扩大这些多数类的主导地位,从而导致次级分类性能。在这项工作中,我们试图通过生成少数族裔类实例来平衡培训数据,从而扩展了以前的基于过度采样的技术来解决这个问题。此任务是不平凡的,因为这些技术的设计是实例是独立的。忽视关系信息会使此过采样过程变得复杂。此外,节点分类任务通常仅使用少数标记的节点进行半监督设置,从而为少数族裔实例的产生提供了不足的监督。生成的低质量新节点会损害训练有素的分类器。在这项工作中,我们通过在构造的嵌入空间中综合新节点来解决这些困难,该节点编码节点属性和拓扑信息。此外,对边缘生成器进行同时训练,以建模图结构并为新样品提供关系。为了进一步提高数据效率,我们还探索合成的混合``中间''节点在此过度采样过程中利用多数类的节点。对现实世界数据集的实验验证了我们提出的框架的有效性。
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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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Recently, graph anomaly detection has attracted increasing attention in data mining and machine learning communities. Apart from existing attribute anomalies, graph anomaly detection also captures suspicious topological-abnormal nodes that differ from the major counterparts. Although massive graph-based detection approaches have been proposed, most of them focus on node-level comparison while pay insufficient attention on the surrounding topology structures. Nodes with more dissimilar neighborhood substructures have more suspicious to be abnormal. To enhance the local substructure detection ability, we propose a novel Graph Anomaly Detection framework via Multi-scale Substructure Learning (GADMSL for abbreviation). Unlike previous algorithms, we manage to capture anomalous substructures where the inner similarities are relatively low in dense-connected regions. Specifically, we adopt a region proposal module to find high-density substructures in the network as suspicious regions. Their inner-node embedding similarities indicate the anomaly degree of the detected substructures. Generally, a lower degree of embedding similarities means a higher probability that the substructure contains topology anomalies. To distill better embeddings of node attributes, we further introduce a graph contrastive learning scheme, which observes attribute anomalies in the meantime. In this way, GADMSL can detect both topology and attribute anomalies. Ultimately, extensive experiments on benchmark datasets show that GADMSL greatly improves detection performance (up to 7.30% AUC and 17.46% AUPRC gains) compared to state-of-the-art attributed networks anomaly detection algorithms.
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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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图形神经网络(GNNS)在建模图形结构数据方面表明了它们的能力。但是,实际图形通常包含结构噪声并具有有限的标记节点。当在这些图表中培训时,GNN的性能会显着下降,这阻碍了许多应用程序的GNN。因此,与有限标记的节点开发抗噪声GNN是重要的。但是,这是一个相当有限的工作。因此,我们研究了在具有有限标记节点的嘈杂图中开发鲁棒GNN的新问题。我们的分析表明,嘈杂的边缘和有限的标记节点都可能损害GNN的消息传递机制。为减轻这些问题,我们提出了一种新颖的框架,该框架采用嘈杂的边缘作为监督,以学习去噪和密集的图形,这可以减轻或消除嘈杂的边缘,并促进GNN的消息传递,以缓解有限标记节点的问题。生成的边缘还用于规则地将具有标记平滑度的未标记节点的预测规范化,以更好地列车GNN。实验结果对现实世界数据集展示了在具有有限标记节点的嘈杂图中提出框架的稳健性。
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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。
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链接预测是一项重要的任务,在各个域中具有广泛的应用程序。但是,大多数现有的链接预测方法都假定给定的图遵循同质的假设,并设计基于相似性的启发式方法或表示学习方法来预测链接。但是,许多现实世界图是异性图,同义假设不存在,这挑战了现有的链接预测方法。通常,在异性图中,有许多引起链接形成的潜在因素,并且两个链接的节点在一个或两个因素中往往相似,但在其他因素中可能是不同的,导致总体相似性较低。因此,一种方法是学习每个节点的分离表示形式,每个矢量捕获一个因子上的节点的潜在表示,这铺平了一种方法来模拟异性图中的链接形成,从而导致更好的节点表示学习和链接预测性能。但是,对此的工作非常有限。因此,在本文中,我们研究了一个新的问题,该问题是在异性图上进行链接预测的分离表示学习。我们提出了一种新颖的框架分解,可以通过建模链接形成并执行感知因素的消息来学习以促进链接预测来学习解开的表示形式。在13个现实世界数据集上进行的广泛实验证明了Disenlink对异性恋和血友病图的链接预测的有效性。我们的代码可从https://github.com/sjz5202/disenlink获得
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众所周知,图形神经网络(GNN)的成功高度依赖于丰富的人类通知数据,这在实践中努力获得,并且并非总是可用的。当只有少数标记的节点可用时,如何开发高效的GNN仍在研究。尽管已证明自我训练对于半监督学习具有强大的功能,但其在图形结构数据上的应用可能会失败,因为(1)不利用较大的接收场来捕获远程节点相互作用,这加剧了传播功能的难度 - 标记节点到未标记节点的标签模式; (2)有限的标记数据使得在不同节点类别中学习良好的分离决策边界而不明确捕获基本的语义结构,这是一项挑战。为了解决捕获信息丰富的结构和语义知识的挑战,我们提出了一个新的图数据增强框架,AGST(增强图自训练),该框架由两个新的(即结构和语义)增强模块构建。 GST骨干。在这项工作中,我们研究了这个新颖的框架是否可以学习具有极有限标记节点的有效图预测模型。在有限标记节点数据的不同情况下,我们对半监督节点分类进行全面评估。实验结果证明了新的数据增强框架对节点分类的独特贡献,几乎没有标记的数据。
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Graph Neural Networks (GNNs) have been widely applied in the semi-supervised node classification task, where a key point lies in how to sufficiently leverage the limited but valuable label information. Most of the classical GNNs solely use the known labels for computing the classification loss at the output. In recent years, several methods have been designed to additionally utilize the labels at the input. One part of the methods augment the node features via concatenating or adding them with the one-hot encodings of labels, while other methods optimize the graph structure by assuming neighboring nodes tend to have the same label. To bring into full play the rich information of labels, in this paper, we present a label-enhanced learning framework for GNNs, which first models each label as a virtual center for intra-class nodes and then jointly learns the representations of both nodes and labels. Our approach could not only smooth the representations of nodes belonging to the same class, but also explicitly encode the label semantics into the learning process of GNNs. Moreover, a training node selection technique is provided to eliminate the potential label leakage issue and guarantee the model generalization ability. Finally, an adaptive self-training strategy is proposed to iteratively enlarge the training set with more reliable pseudo labels and distinguish the importance of each pseudo-labeled node during the model training process. Experimental results on both real-world and synthetic datasets demonstrate our approach can not only consistently outperform the state-of-the-arts, but also effectively smooth the representations of intra-class nodes.
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