由于其在许多有影响力的领域中的广泛应用,归因网络上的图形异常检测已成为普遍的研究主题。在现实情况下,属性网络中的节点和边缘通常显示出不同的异质性,即不同类型的节点的属性显示出大量的多样性,不同类型的关系表示多种含义。在这些网络中,异常在异质性的各个角度上的表现通常与大多数不同。但是,现有的图异常检测方法不能利用归因网络中的异质性,这与异常检测高度相关。鉴于这个问题,我们提出了前方的提议:基于编码器解码器框架的异质性无监督图异常检测方法。具体而言,对于编码器,我们设计了三个关注级别,即属性级别,节点类型级别和边缘级别的关注,以捕获网络结构的异质性,节点属性和单个节点的信息。在解码器中,我们利用结构,属性和节点类型重建项来获得每个节点的异常得分。广泛的实验表明,与无监督环境中的艺术品相比,在几个现实世界中的异质信息网络上,前方的优势。进一步的实验验证了我们三重注意力,模型骨干和解码器的有效性和鲁棒性。
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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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异常检测属性网络广泛用于Web购物,金融交易,通信网络等。但是,大多数工作都试图仅考虑单个交互动作,检测属性网络上的异常,这不能考虑在多视图归属网络中的丰富种类的交互动作。事实上,它仍然是一个具有挑战性的任务,可以均匀地考虑所有不同类型的交互动作,并检测多视图归属网络中的异常情况。在本文中,我们提出了一个基于图形卷积的框架,Anomman,以检测\ textBF {Anom} Aly On \ TextBF {M} Ulti-View \ TextBF {A} Ttributed \ TextBF {n} etworks。要在共同考虑属性和所有交互操作,我们使用注意机制来定义网络中所有视图的重要性。此外,由于其低通特性,图形卷积操作不能简单地应用于异常检测任务。因此,Anomman使用图形自动编码器模块来克服缺点并将其转化为我们的实力。根据真实世界数据集的实验,Anomman优于最先进的模型和我们所提出的模型的两个变体。此外,Anomman的精度@ 50指示器在数据集上达到1.000,这表明由Anomman检测到的前50个异常实例是所有异常的。
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近年来,由于其在金融,网络安全和医学等广泛的领域中的应用,近年来,归因网络中的异常检测受到了极大的关注。传统方法不能在属性网络的设置上采用以解决异常检测问题。这种方法的主要局限性是它们固有地忽略了数据特征之间的关系信息。随着基于深度学习和图神经网络技术的快速爆炸,由于深度技术在提取复杂关系方面的潜力,因此在归因网络上发现稀有对象已大大发展。在本文中,我们提出了有关异常检测的新架构。设计这种体系结构的主要目标是利用多任务学习,以增强检测性能。基于多任务的基于学习的异常检测仍处于起步阶段,现有文献中只有少数研究迎合了同样的研究。我们合并了社区检测和多视图表示学习技术,以从属性网络中提取明显和互补的信息,并随后融合捕获的信息以获得更好的检测结果。该体系结构中采用的两个主要组成部分(即社区特定的学习和多视图表示学习)之间的相互合作展示了一种有希望的解决方案,以达到更有效的结果。
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归因网络上的异常检测最近在许多研究领域(例如控制论异常检测和财务欺诈检测)受到了越来越多的关注。随着深度学习在图表表示上的广泛应用,现有的方法选择将欧几里得图编码器作为骨架进行应用,这可能会失去重要的层次结构信息,尤其是在复杂的网络中。为了解决这个问题,我们建议使用双曲线自我监督对比度学习有效的异常检测框架。具体而言,我们首先通过执行子图抽样进行数据增强。然后,我们通过指数映射和对数映射利用双曲线空间中的分层信息,并通过通过区分过程从负对中减去正对的分数来获得异常得分。最后,在四个现实世界数据集上进行的广泛实验表明,我们的方法在代表性基线方法上的表现优越。
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Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. To be specific, we regard the original input graph as the first view and generate the second view by graph augmentation with edge modifications. With the guidance of maximizing the similarity of the subgraph pairs, the proposed subgraph-subgraph contrast contributes to more robust subgraph embeddings despite of the structure variation. Moreover, the introduced subgraph-subgraph contrast cooperates well with the widely-adopted node-subgraph and node-node contrastive counterparts for mutual GAD performance promotions. Besides, we also conduct sufficient experiments to investigate the impact of different graph augmentation approaches on detection performance. The comprehensive experimental results well demonstrate the superiority of our method compared with the state-of-the-art approaches and the effectiveness of the multi-view subgraph pair contrastive strategy for the GAD task.
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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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Anomaly detection is defined as discovering patterns that do not conform to the expected behavior. Previously, anomaly detection was mostly conducted using traditional shallow learning techniques, but with little improvement. As the emergence of graph neural networks (GNN), graph anomaly detection has been greatly developed. However, recent studies have shown that GNN-based methods encounter challenge, in that no graph anomaly detection algorithm can perform generalization on most datasets. To bridge the tap, we propose a multi-view fusion approach for graph anomaly detection (Mul-GAD). The view-level fusion captures the extent of significance between different views, while the feature-level fusion makes full use of complementary information. We theoretically and experimentally elaborate the effectiveness of the fusion strategies. For a more comprehensive conclusion, we further investigate the effect of the objective function and the number of fused views on detection performance. Exploiting these findings, our Mul-GAD is proposed equipped with fusion strategies and the well-performed objective function. Compared with other state-of-the-art detection methods, we achieve a better detection performance and generalization in most scenarios via a series of experiments conducted on Pubmed, Amazon Computer, Amazon Photo, Weibo and Books. Our code is available at https://github.com/liuyishoua/Mul-Graph-Fusion.
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与其他图表相比,图形级异常检测(GAD)描述了检测其结构和/或其节点特征的图表的问题。GAD中的一个挑战是制定图表表示,该图表示能够检测本地和全局 - 异常图,即它们的细粒度(节点级)或整体(图级)属性异常的图形,分别。为了解决这一挑战,我们介绍了一种新的深度异常检测方法,用于通过图表和节点表示的联合随机蒸馏学习丰富的全球和局部正常模式信息。通过训练一个GNN来实现随机初始化网络权重的另一GNN来实现随机蒸馏。来自各种域的16个真实图形数据集的广泛实验表明,我们的模型显着优于七种最先进的模型。代码和数据集可以在https://git.io/llocalkd中获得。
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用于异质图嵌入的图形神经网络是通过探索异质图的异质性和语义来将节点投射到低维空间中。但是,一方面,大多数现有的异质图嵌入方法要么不足以对特定语义下的局部结构进行建模,要么在汇总信息时忽略异质性。另一方面,来自多种语义的表示形式未全面整合以获得多功能节点嵌入。为了解决该问题,我们通过引入多视图表示学习的概念,提出了一个具有多视图表示学习(名为MV-HETGNN)的异质图神经网络(称为MV-HETGNN)。所提出的模型由节点特征转换,特定于视图的自我图编码和自动多视图融合,以彻底学习复杂的结构和语义信息,以生成全面的节点表示。在三个现实世界的异质图数据集上进行的广泛实验表明,所提出的MV-HETGNN模型始终优于各种下游任务中所有最新的GNN基准,例如节点分类,节点群集和链接预测。
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图形离群值检测是一项具有许多应用程序的新兴但至关重要的机器学习任务。尽管近年来算法扩散,但缺乏标准和统一的绩效评估设置限制了它们在现实世界应用中的进步和使用。为了利用差距,我们(据我们所知)(据我们所知)第一个全面的无监督节点离群值检测基准为unod,并带有以下亮点:(1)评估骨架从经典矩阵分解到最新图形神经的骨架的14个方法网络; (2)在现实世界数据集上使用不同类型的注射异常值和自然异常值对方法性能进行基准测试; (3)通过在不同尺度的合成图上使用运行时和GPU存储器使用算法的效率和可扩展性。基于广泛的实验结果的分析,我们讨论了当前渠道方法的利弊,并指出了多个关键和有希望的未来研究方向。
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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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对于由硬件和软件组件组成的复杂分布式系统而言,异常检测是一个重要的问题。对此类系统的异常检测的要求和挑战的透彻理解对于系统的安全性至关重要,尤其是对于现实世界的部署。尽管有许多解决问题的研究领域和应用领域,但很少有人试图对这种系统进行深入研究。大多数异常检测技术是针对某些应用域的专门开发的,而其他检测技术则更为通用。在这项调查中,我们探讨了基于图的算法在复杂分布式异质系统中识别和减轻不同类型异常的重要潜力。我们的主要重点是在分布在复杂分布式系统上的异质计算设备上应用时,可深入了解图。这项研究分析,比较和对比该领域的最新研究文章。首先,我们描述了现实世界分布式系统的特征及其在复杂网络中的异常检测的特定挑战,例如数据和评估,异常的性质以及现实世界的要求。稍后,我们讨论了为什么可以在此类系统中利用图形以及使用图的好处。然后,我们将恰当地深入研究最先进的方法,并突出它们的优势和劣势。最后,我们评估和比较这些方法,并指出可能改进的领域。
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近年来,由于其在研究和实践中的重要性,对归属网络的异常检测问题有望的兴趣。虽然已经提出了各种方法来解决这个问题,但存在两种主要限制:(1)由于缺乏监控信号,未经监督的方法通常会效率低得多,(2)现有的异常检测方法仅使用本地语境信息来检测异常信息以检测异常信息节点,例如,单跳或两跳信息,但忽略全局上下文信息。由于异常节点与结构和属性中的正常节点不同,因此如果我们删除连接异常和正常节点的边缘,异常节点和其邻居之间的距离应该大于正常节点和其邻居之间的距离直观。因此,基于全局和本地上下文信息的跳数可以作为异常的指标。通过这种直觉激励,我们提出了一种基于跳数的模型(HCM)来通过建模本地和全局上下文信息来检测异常。为了更好地利用异常识别的跳跃计数,我们建议使用跳数预测作为自我监督任务。我们根据HOP计数通过HCM模型设计了两个异常的分数来识别异常。此外,我们雇用贝叶斯学习培训HCM模型,以捕获学习参数的不确定性,避免过度装备。关于现实世界归属网络的广泛实验表明,我们所提出的模型在异常检测中是有效的。
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Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.
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生成的自我监督学习(SSL),尤其是蒙面自动编码器,已成为最令人兴奋的学习范式之一,并且在处理图形数据方面表现出了巨大的潜力。但是,现实世界图总是异质的,它提出了现有方法忽略的三个关键挑战:1)如何捕获复杂的图形结构? 2)如何合并各种节点属性? 3)如何编码不同的节点位置?鉴于此,我们研究了异质图上生成SSL的问题,并提出了HGMAE,这是一种新型的异质图掩盖自动编码器模型,以应对这些挑战。 HGMAE通过两种创新的掩蔽技术和三种独特的培训策略捕获了全面的图形信息。特别是,我们首先使用动态掩模速率开发Metapath掩盖和自适应属性掩蔽,以实现在异质图上有效和稳定的学习。然后,我们设计了几种培训策略,包括基于Metapath的边缘重建,以采用复杂的结构信息,目标属性恢复以结合各种节点属性,以及位置特征预测以编码节点位置信息。广泛的实验表明,HGMAE在多个数据集上的几个任务上均优于对比度和生成的最新基准。
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Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
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最近的研究表明,基于自动编码器的模型可以在异常检测任务上实现出色的性能,因为它们以无监督的方式适合复杂数据的能力出色。在这项工作中,我们提出了一种新型的基于自动编码器的模型,称为Stackvae-G,可以显着将效率和解释性带入多元时间序列异常检测。具体而言,我们通过使用权重共生方案的堆叠式重建来利用整个时间序列频道的相似性来减少学习模型的大小,并减轻培训数据中未知噪声的过度拟合。我们还利用图形学习模块来学习稀疏的邻接矩阵,以明确捕获多个时间序列通道之间的稳定相互关系结构,以便对相互关联的通道的可解释模式重建。结合了这两个模块,我们将堆叠式块VAE(变异自动编码器)与GNN(图神经网络)模型进行了多变量时间序列异常检测。我们对三个常用的公共数据集进行了广泛的实验,这表明我们的模型与最先进的模型相当(甚至更好)的性能,同时需要更少的计算和内存成本。此外,我们证明,通过模型学到的邻接矩阵可以准确捕获多个渠道之间的相互关系,并可以为失败诊断应用提供有价值的信息。
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近年来,多视图学习迅速发展。尽管许多先前的研究都认为每个实例都出现在所有视图中,但在现实世界应用程序中很常见,从某些视图中丢失实例,从而导致多视图数据不完整。为了解决这个问题,我们提出了一个新型潜在的异质图网络(LHGN),以实现不完整的多视图学习,该学习旨在以灵活的方式尽可能充分地使用多个不完整的视图。通过学习统一的潜在代表,隐含地实现了不同观点之间一致性和互补性之间的权衡。为了探索样本与潜在表示之间的复杂关系,首次提出了邻域约束和视图约束,以构建异质图。最后,为了避免训练和测试阶段之间的任何不一致之处,基于图形学习的分类任务应用了转导学习技术。对现实世界数据集的广泛实验结果证明了我们模型对现有最新方法的有效性。
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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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