Fangfang Shan^1,2,*, Fuyang Li¹, Zhenyu Wang¹, Peiyu Ji¹, Mengyi Wang¹, Huifang Sun¹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1029-1044, 2024, DOI:10.32604/cmes.2024.047665

Abstract A deep learning access control model based on user preferences is proposed to address the issue of personal privacy leakage in social networks. Firstly, social users and social data entities are extracted from the social network and used to construct homogeneous and heterogeneous graphs. Secondly, a graph neural network model is designed based on user daily social behavior and daily social data to simulate the dissemination and changes of user social preferences and user personal preferences in the social network. Then, high-order neighbor nodes, hidden neighbor nodes, displayed neighbor nodes, and social data nodes are used to update user nodes… More >

J. Maria Regila Baby^*, K. Uma Samundesvari

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2405-2415, 2023, DOI:10.32604/csse.2023.032240

Abstract Many graph domination applications can be expanded to achieve complete cototal domination. If every node in a dominating set is regarded as a record server for a PC organization, then each PC affiliated with the organization has direct access to a document server. It is occasionally reasonable to believe that this gateway will remain available even if one of the scrape servers fails. Because every PC has direct access to at least two documents’ servers, a complete cototal dominating set provides the required adaptability to non-critical failure in such scenarios. In this paper, we presented a method for calculating a… More >

Chunjing Xiao^1,∗, Yongwei Qiao², Kewen Xia¹, Yuxiang Zhang³

Computer Systems Science and Engineering, Vol.33, No.2, pp. 95-103, 2018, DOI:10.32604/csse.2018.33.095

Abstract Tourist routes recommendation is a way to improve the tourist experience and the efficiency of tourism companies. Session-based methods divide all users’ interaction histories into the same number sessions with fixed time window and treat the user preference as time sequences. There have few or even no interaction in some sessions for some users because of the high sparsity and temporal characteristics of tourist data. That lead to many session-based methods can not be applied to routes recommendation due to aggravate the sparsity. In order to better adapt and apply the characteristics of tourism data and alleviate the sparsity, a… More >