Deep Graph neural network-based spammer detection under the perspective of heterogeneous cyberspace

Zhiwei Guo, Lianggui Tang, Tan Guo, Keping Yu*, Mamoun Alazab, Andrii Shalaginov

*この研究の対応する著者

研究成果: Article査読

9 被引用数 (Scopus)

抄録

Due to the severe threat to cyberspace security, detection of online spammers has been a universal concern of academia. Nowadays, prevailing literature of this field almost leveraged various relations to enhance feature spaces. However, they majorly focused stable or visible relations, yet neglected the existence of those which are generated occasionally. Exactly, some latent feature components can be extracted from the view of heterogeneous information networks. Thus, this paper proposes a Deep Graph neural network-based Spammer detection (DeG-Spam) model under the perspective of heterogeneous cyberspace. Specifically, representations for occasional relations and inherent relations are separately modelled. Based on this, a graph neural network framework is formulated to generate feature expressions for the social graph. With more feature components being mined, acquirement of stronger and more comprehensive feature spaces ensures the accuracy of spammer detection. At last, fruitful experiments are carried out on two benchmark datasets to compare the DeG-Spam with typical spammer detection approaches. Experimental results show that it performs about 5%–10% better than baselines.

本文言語English
ページ(範囲)205-218
ページ数14
ジャーナルFuture Generation Computer Systems
117
DOI
出版ステータスPublished - 2021 4

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信

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