Title: Node anomaly detection in social networks using cohesive non-local graph convolutional network

Authors: Yallamanda Rajesh Babu; G. Karthick; V.V. Jaya Rama Krishnaiah

Addresses: Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar – 608002, Tamil Nādu, India ' Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar – 608002, Tamil Nādu, India ' Department of Computer Science and Engineering, ASN Women's Engineering College, Tenali, Andhra Pradesh, India

Abstract: Users connect with one another and develop relationships on social media platforms. These users have a collection of personal information about themselves on these platforms and communicate with one another. Social networks are becoming more prevalent all across the globe. With all of its advantages, criminality and fraudulent conduct in this medium are on the rise. As a result, there is an urgent need to detect abnormalities in these networks before they do substantial harm. Social network analysis uses graph data structure to represent and manage data. Graphs store data and capture relationships that exist between the nodes. Graphs are a complicated kind of data representation in which each data entry contains attributes and is also connected to other data entries traditional non-deep learning approaches are failing to perform effectively when the size and scope of real-world social networks rise in numbers.

Keywords: anomaly detection; graph; node anomaly; graph convolutional network; GCN; auto-encoder; CNLGCN.

DOI: 10.1504/IJCVR.2026.150339

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.1, pp.55 - 66

Received: 10 Nov 2023
Accepted: 17 Dec 2023
Published online: 10 Dec 2025 *

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