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An efficient framework for anomaly detection in attributed social networks

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Abstract

Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in a number of high-impact areas. The majority of current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method. These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders suffer from the problem of ignoring the latent codes’ embedding distribution, which results in poor representation in many instances. In this paper, we present a new framework for detecting anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for considering the potential data distribution. Lastly, the Gaussian Mixture Model is used to approximate the latent vector density, resulting in anomaly detection from measuring sample energy. Extensive experiments on attributed networks prove that the proposed framework significantly outperforms the existing methods, proving the efficiency of the proposed approach.

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Acknowledgements

This work is acknowledged under Integral University manuscript No IU/R&D/2022-MCN0001490.

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Authors and Affiliations

  1. Integral University, Lucknow, India

    Wasim Khan & Mohammad Haroon

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  1. Wasim Khan
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  2. Mohammad Haroon
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Correspondence to Wasim Khan.

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Khan, W., Haroon, M. An efficient framework for anomaly detection in attributed social networks. Int. j. inf. tecnol. 14, 3069–3076 (2022). https://doi.org/10.1007/s41870-022-01044-2

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