Community-based anomaly detection using spectral graph filtering

Francisquini, Rodrigo; Lorena, Ana Carolina; Nascimento, Mariá C. V.

doi:10.1016/j.asoc.2022.108489

Cited by 11 publications

(2 citation statements)

References 22 publications

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“…The primary data source is the municipality of São José dos Campos, which provided to us, under confidentiality agreement, a desegregated dataset to the patient level, reporting all COVID-19 cases in the city and how they have evolved. This data was partially used in a recent study [32] . Thus, we aggregated all the information on a daily level and produced the following first two variables described in each subsection that follows.…”

Section: Case Study: the Datamentioning

confidence: 99%

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

Souza

Nascimento

2022

Applied Soft Computing

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Section: Case Study: the Datamentioning

confidence: 99%

COVID-19 ICU demand forecasting: A two-stage Prophet-LSTM approach

Souza

Nascimento

2022

Applied Soft Computing

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“…The anomaly of node mode is mainly reflected in two aspects: first, abnormal connection structures around nodes; second, abnormal attributes of nodes. According to the different information considered in anomaly detection, the existing anomaly detection methods can be divided into three categories: first, anomaly detection methods based on the characteristics of nodes themselves [1]; second, anomaly detection methods based on egonet [2] or community division [3]; third, anomaly detection methods based on network embedding [4]. The existing methods still face the following two problems when dealing with attributed network data: it is difficult to deal with the interaction between structural information and attribute information, and it is difficult to distinguish structural anomaly from attribute anomaly.…”

Section: Introductionmentioning

confidence: 99%

DADDE:deep anomaly detection with density estimation

Duan

Jiao

Yin

et al. 2022

J. Phys.: Conf. Ser.

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The problem of abnormal nodes detection in attributed network is widely used in daily life, such as social networks, cyberspace security, financial fields and so on. Most existing detection methods ignore the relationship between structure information and attribute information in attributed network. Although some methods consider the relationship between them, but it can’t distinguish the types of abnormal nodes well, that is, attribute exceptions or structural exceptions. Aiming at the shortcomings of existing methods, this paper proposes a deep anomaly detection model combining density estimation. The idea of detecting abnormal nodes by reconstruction error is used, the structure information and attribute information of attributed network are reconstructed respectively, and abnormal nodes are detected by density estimation based on reconstruction error and embedding vector of nodes. In this paper, the validity of DADDE model is proved by experiments on three commonly used data sets in this field.

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