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research-article

An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

Authors:

Seyed Taghi Akhavan NiakiAuthors Info & Claims

Volume 171, Issue C

Pages 497 - 511

https://doi.org/10.1016/j.neunet.2023.12.024

Published: 01 March 2024 Publication History

Abstract

Online monitoring of social networks offers exciting features for platforms, enabling both technical and behavioral analysis. Numerous studies have explored the adaptation of traditional quality control methods for detecting change points within social networks. However, the current research studies face limitations such as an overreliance on case-based attributes, high computational costs, poor scalability with large networks, and low sensitivity in fast change point detection. This paper proposes a novel algorithm for social network monitoring using One-Class Support Vector Machines (OC-SVMs) to address these limitations. Additionally, using both nodal and network-level attributes makes it versatile for diverse social network applications and effectively detecting network disturbances. The algorithm utilizes a well-defined training data dictionary with an updating procedure for evolutionary networks, enhancing memory and time efficiency by reducing the processing of input data. Extensive numerical experiments are conducted using an EpiCNet model to simulate interactions in an online social network, covering six change scenarios to evaluate the proposed methodology. The results show lower Average Run Length (ARL) and Expected Delay Detection (EDD), demonstrating the superior accuracy and effectiveness of the OC-SVM algorithm compared to alternative methods. Applying OC-SVM to the Enron Email network indicates its capability to identify change points, reflecting the tumultuous timeline that led to Enron's downfall. This further validates the substantial advancement of OC-SVM in social network monitoring and opens doors to broader real-world applications.

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cover image Neural Networks

Neural Networks Volume 171, Issue C

Mar 2024

514 pages

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Elsevier Ltd.

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Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 March 2024

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