Anomaly Detection in Time-Evolving Attributed Networks

Xue, Luguo; Luo, Minnan; Peng, Zhen; Li, Jundong; Chen, Yan; Liu, Jun

doi:10.1007/978-3-030-18590-9_19

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…One of the important tasks that can be performed on dynamic networks is finding anomalous users and attributes in social networks. Due to the nature of dynamic networks, the following challenges are introduced in addition to the anomalies found in static networks [82] [112]:…”

Section: Anomaly Detection In Dynamic Networkmentioning

confidence: 99%

Recent Progress on Anomaly Detection in Social Networks using Graph Neural Networks

Daniel¹

2022

Preprint

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Section: Anomaly Detection In Dynamic Networkmentioning

confidence: 99%

Recent Progress on Anomaly Detection in Social Networks using Graph Neural Networks

Daniel¹

2022

Preprint

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“…The communities emerge across snapshots. The emerging community anomalies can be described as objects that evolve dissimilarly rather than following the community change trends [81,82]. These community anomalies are based on stream graph clustering algorithms.…”

Section: ) Anomalous Subgraphs or Anomalous Communitiesmentioning

confidence: 99%

Recent Progress on Anomaly Detection in Social Networks using Graph Neural Networks

Daniel¹

2022

Preprint

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“…Compared with the plain (unlabeled) network, the attributed networks can model complex systems more effectively due to containing richer attribute information. Therefore, lots of researchers began to show interest in the problem of anomaly detection on attributed networks [16]- [18]. For example, Perozzi et al [19] leveraged attributes and network structure to quantify the quality of neighborhoods so that find anomalous neighborhoods on attributed networks.…”

Section: Related Workmentioning

confidence: 99%

Higher-order Structure Based Anomaly Detection on Attributed Networks

Zhou

et al. 2021

2021 IEEE International Conference on Big Data (Big Data)

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Anomaly detection (such as telecom fraud detection and medical image detection) has attracted the increasing attention of people. The complex interaction between multiple entities widely exists in the network, which can reflect specific human behavior patterns. Such patterns can be modeled by higher-order network structures, thus benefiting anomaly detection on attributed networks. However, due to the lack of an effective mechanism in most existing graph learning methods, these complex interaction patterns fail to be applied in detecting anomalies, hindering the progress of anomaly detection to some extent. In order to address the aforementioned issue, we present a higher-order structure based anomaly detection (GUIDE) method. We exploit attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures, respectively. Moreover, we design a graph attention layer to evaluate the significance of neighbors to nodes through their higher-order structure differences. Finally, we leverage node attribute and higher-order structure reconstruction errors to find anomalies. Extensive experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly outperforms the state-of-art methods.

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Anomaly Detection in Time-Evolving Attributed Networks

Cited by 5 publications

References 6 publications

Recent Progress on Anomaly Detection in Social Networks using Graph Neural Networks

Recent Progress on Anomaly Detection in Social Networks using Graph Neural Networks

Recent Progress on Anomaly Detection in Social Networks using Graph Neural Networks

Higher-order Structure Based Anomaly Detection on Attributed Networks

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