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• W4224033482 abstract "A spammer sends many useless advertisements to recipients via social networking sites without their permission, posing a serious threat to the information security of regular users, as well as the credit system of these sites. A social network Spammer detection technology based on graph convolution networks (GCNs) is presented with the goal of addressing the shortcomings of existing social network Spammer detection technologies, such as their shallow feature extraction and excessive computational complexity. With the help of an introduction of a network representation learning algorithm, this method extracts the local structural features of the network and then combines the GCN algorithm with renormalization technology to obtain the global structural features of the network, which can be used to detect spam. Experiments conducted on data from the social networking site demonstrate that the proposed method has good accuracy and efficiency." @default.
• W4224033482 created "2022-04-19" @default.
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• W4224033482 date "2022-04-14" @default.
• W4224033482 modified "2023-10-17" @default.
• W4224033482 title "Graph based CNN Algorithm to Detect Spammer Activity Over Social Media" @default.
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• W4224033482 doi "https://doi.org/10.1080/03772063.2022.2061610" @default.
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