Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2024.056930
Journal Menu
Special Issues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Unmasking Social Robots’ Camouflage: A GNN-Random Forest Framework for Enhanced Detection

Weijian Fan1,*, Chunhua Wang2, Xiao Han3, Chichen Lin4
1 School of Data Science and Intelligent Media, Communication University of China, Beijing, 100024, China
2 School of Computer and Cyber Sciences, Communication University of China, Beijing, 100024, China
3 Institute of Communication Studies, Communication University of China, Beijing, 100024, China
4 State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, 100024, China
* Corresponding Author: Weijian Fan. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.056930

Received 02 August 2024; Accepted 12 October 2024; Published online 11 November 2024

Abstract

The proliferation of robot accounts on social media platforms has posed a significant negative impact, necessitating robust measures to counter network anomalies and safeguard content integrity. Social robot detection has emerged as a pivotal yet intricate task, aimed at mitigating the dissemination of misleading information. While graph-based approaches have attained remarkable performance in this realm, they grapple with a fundamental limitation: the homogeneity assumption in graph convolution allows social robots to stealthily evade detection by mingling with genuine human profiles. To unravel this challenge and thwart the camouflage tactics, this work proposed an innovative social robot detection framework based on enhanced HOmogeneity and Random Forest (HORFBot). At the core of HORFBot lies a homogeneous graph enhancement strategy, intricately woven with edge-removal techniques, to meticulously dissect the graph into multiple revealing subgraphs. Subsequently, leveraging the power of contrastive learning, the proposed methodology meticulously trains multiple graph convolutional networks, each honed to discern nuances within these tailored subgraphs. The culminating stage involves the fusion of these feature-rich base classifiers, harmoniously aggregating their insights to produce a comprehensive detection outcome. Extensive experiments on three social robot detection datasets have shown that this method effectively improves the accuracy of social robot detection and outperforms comparative methods.

Keywords

Social robot detection; graph neural networks; random forest; homophily; heterophily

  • 260

    View

  • 49

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link