research-article

RoSGAS: Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search

Authors:

Yingguang Yang,

Haiyong XieAuthors Info & Claims

ACM Transactions on the Web, Volume 17, Issue 3

Article No.: 15, Pages 1 - 31

https://doi.org/10.1145/3572403

Published: 22 May 2023 Publication History

Abstract

Social bots are referred to as the automated accounts on social networks that make attempts to behave like humans. While Graph Neural Networks (GNNs) have been massively applied to the field of social bot detection, a huge amount of domain expertise and prior knowledge is heavily engaged in the state-of-the-art approaches to design a dedicated neural network architecture for a specific classification task. Involving oversized nodes and network layers in the model design, however, usually causes the over-smoothing problem and the lack of embedding discrimination. In this article, we propose RoSGAS, a novel Reinforced and Self-supervised GNN Architecture Search framework to adaptively pinpoint the most suitable multi-hop neighborhood and the number of layers in the GNN architecture. More specifically, we consider the social bot detection problem as a user-centric subgraph embedding and classification task. We exploit the heterogeneous information network to present the user connectivity by leveraging account metadata, relationships, behavioral features, and content features. RoSGAS uses a multi-agent deep reinforcement learning (RL), 31 pages. mechanism for navigating the search of optimal neighborhood and network layers to learn individually the subgraph embedding for each target user. A nearest neighbor mechanism is developed for accelerating the RL training process, and RoSGAS can learn more discriminative subgraph embedding with the aid of self-supervised learning. Experiments on five Twitter datasets show that RoSGAS outperforms the state-of-the-art approaches in terms of accuracy, training efficiency, and stability and has better generalization when handling unseen samples.

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Index Terms

RoSGAS: Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 17, Issue 3

August 2023

302 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3597636

Editor:
Ryen White
Microsoft Research, USA

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Publication History

Published: 22 May 2023

Online AM: 02 December 2022

Accepted: 20 October 2022

Revised: 14 June 2022

Received: 25 January 2022

Published in TWEB Volume 17, Issue 3

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National Key R&D Program of China
S&T Program of Hebei
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