MixedAD: A Scalable Algorithm for Detecting Mixed Anomalies in Attributed Graphs

Zhu, Mengxiao; Zhu, Haogang

doi:10.1609/aaai.v34i01.5482

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…Further, ANOMALOUS [18] jointly considers CUR decomposition and residual analysis for anomaly detection on attributed networks. Zhu et al [49] present a joint learning model to detect mixed anomaly by core initiating and expanding. Despite their success on low-dimensional attributed network data, these methods cannot work well when the networks have complex structures and high-dimensional attributes due to the limitation of their shallow mechanisms.…”

Section: B Anomaly Detection On Attributed Networkmentioning

confidence: 99%

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Liu

Pan

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

190

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Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from highdimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt Manuscript

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Section: B Anomaly Detection On Attributed Networkmentioning

confidence: 99%

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Liu

Pan

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

190

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show abstract

“…When detecting anomalous nodes in static graphs, the differences between anomalies and regular nodes are mainly drawn from the graph structural information and nodes/edges' attributes. Given the prior knowledge (i.e., community structure, attributes) about a static graph, anomalous nodes can be further categorized into the following three types [39], [69], [70]:…”

Section: Anomalous Node Detection (Anos Nd)mentioning

confidence: 99%

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Ma,

Wu,

Xue

et al. 2021

Preprint

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Anomalies represent rare observations (e.g., data records, messages or events) that are deviating significantly from others. Over the last forty years, researches on anomalies have received intensified interests and the burst of information has attracted more attention on anomalies because of their significance in a wide range of disciplines (e.g., computer science, chemistry, and biology). Anomaly detection, which aims to identify these rare observations, is among the most vital tasks and has shown its power in preventing detrimental events, such as financial fraud, network intrusion, and social spam, from happening. The detection task is typically solved by detecting outlying data points in the features space and inherently overlooks the structural information in real-world data. Graphs have been prevalently used to preserve the structural information, and this raises the graph anomaly detection problem -identifying anomalous graph objects (i.e., nodes, edges and sub-graphs). However, conventional anomaly detection techniques cannot well solve this problem because of the complexity of graph data (e.g., irregular structures, non-independent and large-scale). For the aptitudes of deep learning in breaking these limitations, graph anomaly detection with deep learning has received intensified studies recently. In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection. Specifically, our categorization follows a task-driven strategy and classifies existing works according to the anomalous graph objects they can detect. We especially focus on the motivations, key intuitions and technical details of existing works. Moreover, we summarize open-sourced implementations, public datasets, and commonly-used evaluation metrics to provide affluent resources for future studies. Finally, we highlight twelve extensive future research directions according to our survey results covering emerging problems introduced by graph data, anomaly detection and real applications.

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Contrastive Attributed Network Anomaly Detection with Data Augmentation

Huang

Zhao

et al. 2022

Lecture Notes in Computer Science

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MixedAD: A Scalable Algorithm for Detecting Mixed Anomalies in Attributed Graphs

Cited by 7 publications

References 26 publications

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

Contrastive Attributed Network Anomaly Detection with Data Augmentation

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