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

TL;DR

This paper introduces a neural architecture search method tailored for graph neural networks to improve bot detection on social media, leveraging graph structures and metadata for higher accuracy.

Contribution

It presents DFG-NAS, a novel NAS technique for RGCNs that automatically optimizes architecture components for social media bot detection.

Findings

Achieved 85.7% accuracy on TwiBot-20 dataset.

Surpassed state-of-the-art bot detection models.

Demonstrated effectiveness of NAS in graph neural network design.

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.