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

• URL: http://arxiv.org/abs/2411.16285v1
• Date: Mon, 25 Nov 2024 11:10:16 GMT
• Title: A Graph Neural Architecture Search Approach for Identifying Bots in Social Media
• Authors: Georgios Tzoumanekas, Michail Chatzianastasis, Loukas Ilias, George Kiokes, John Psarras, Dimitris Askounis,
• Abstract summary: We introduce the implementation of a Neural Architecture Search (NAS) technique, tailored to Graph Convolutional Networks (RGCs) 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 RGCs. 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.
• Score: 6.811604745219853
• License:
• Abstract: 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.

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