Unsupervised Anomaly Detection on Node Attributed Networks: A Deep Learning Approach

Parsa Kavehzadeh, Mohammadreza Samadi, Maryam Amir Haeri

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Anomaly detection has been one of the important issues in social network analysis in recent years due to the crucial role it plays in different applications such as fraud and spammer detection. Using both graph and node characteristics leads to more accurate results in detecting anomalous nodes of node attributed networks. Most of the research works in this field are concentrated on supervised methods for anomaly detection. However, in real-world problems, there is not enough labeled data to use supervised methods for anomaly detection. This paper proposes an unsupervised method for detecting anomalous nodes in node attributed networks. The methods used a two-step deep learning approach. In the first step, structural features of the network are extracted using node2vec; in the next step, Variational AutoEncoder (VAE) is used to detect the anomalies considering both structural and node attributes. The anomalous nodes are recognized by their higher reconstruction loss. Our experimental results on two datasets, BlogCatalog and Flickr, show that the suggested method can compete with the state-of-the-art approaches of anomaly detection in attributed networks. Our method (Deep2NAD) outperforms the state-of-the-art result on the Flickr dataset based on AUC. Moreover, it receives an acceptable AUC over the BlogCatalog dataset in comparison to the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Information Science and Systems, ICISS 2021
PublisherAssociation for Computing Machinery (ACM)
Pages35-40
Number of pages6
ISBN (Electronic)9781450389136
DOIs
Publication statusPublished - 17 Mar 2021
Event4th International Conference on Information Science and Systems, ICISS 2021 - Virtual, Online, United Kingdom
Duration: 17 Mar 202119 Mar 2021
Conference number: 4

Conference

Conference4th International Conference on Information Science and Systems, ICISS 2021
Abbreviated titleICISS 2021
CountryUnited Kingdom
CityVirtual, Online
Period17/03/2119/03/21

Keywords

  • Anomaly Detection
  • Attributed Networks
  • Deep Learning
  • Unsupervised Learning

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