Fake account detection based on Node2Vec and node feature merging

Bo Wu; Chungan Huang

doi:10.1117/12.3004673

9 October 2023 Fake account detection based on Node2Vec and node feature merging

Bo Wu, Chungan Huang

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279121 (2023) https://doi.org/10.1117/12.3004673
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Online social network has become an indispensable place for People's Daily life and social communication. Billions of people around the world are more or less active on online social networks every day. While it brings us the convenience of real-time communication and obtaining information, it also hides the threat of fake users in the network. These fake users use malicious comments and spread fake links,seriously affecting the normal experience of users in social networks. The article proposed a fake account detection model based on node2vec and node feature merging that can help accurately identify fake users in online social networks. Experiments showed that the model proposed in this paper has some improvements in accuracy, precision, recall and F1 value compared with other models in fake account detection tasks.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Wu and Chungan Huang "Fake account detection based on Node2Vec and node feature merging", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279121 (9 October 2023); https://doi.org/10.1117/12.3004673

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available