A Graph Neural Architecture Search Approach for Identifying Bots in Social Media

Fuente: arXiv

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Bibliographic Details
Main Authors:	Tzoumanekas, Georgios, Chatzianastasis, Michail, Ilias, Loukas, Kiokes, George, Psarras, John, Askounis, Dimitris
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Social and Information Networks
Online Access:	Acceder al recurso
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_version_	1866913622424289280
author	Tzoumanekas, Georgios Chatzianastasis, Michail Ilias, Loukas Kiokes, George Psarras, John Askounis, Dimitris
author_facet	Tzoumanekas, Georgios Chatzianastasis, Michail Ilias, Loukas Kiokes, George Psarras, John Askounis, Dimitris
contents	Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.
format	Preprint
id	arxiv_https___arxiv_org_abs_2411_16285
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	A Graph Neural Architecture Search Approach for Identifying Bots in Social Media Tzoumanekas, Georgios Chatzianastasis, Michail Ilias, Loukas Kiokes, George Psarras, John Askounis, Dimitris Machine Learning Social and Information Networks Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X. Our model constructs a graph that incorporates both the user relationships and their metadata. Then, DFG-NAS is adapted to automatically search for the optimal configuration of Propagation and Transformation functions in the RGCNs. Our experiments are conducted on the TwiBot-20 dataset, constructing a graph with 229,580 nodes and 227,979 edges. We study the five architectures with the highest performance during the search and achieve an accuracy of 85.7%, surpassing state-of-the-art models. Our approach not only addresses the bot detection challenge but also advocates for the broader implementation of NAS models in neural network design automation.
title	A Graph Neural Architecture Search Approach for Identifying Bots in Social Media
topic	Machine Learning Social and Information Networks
url	https://arxiv.org/abs/2411.16285