The Propagation Background in Social Networks: Simulating and Modeling

• Published in: International journal of automation and computing Vol. 17; no. 3; pp. 353 - 363
• Main Authors: Li, Kai, Xu, Tong, Feng, Shuai, Qiao, Li-Sheng, Shen, Hua-Wei, Lv, Tian-Yang, Cheng, Xue-Qi, Chen, En-Hong
• Format: Journal Article
• Language: English
• Published: Beijing Institute of Automation, Chinese Academy of Sciences 01.06.2020; Springer Nature B.V
• Subjects: Algorithms; CAE) and Design; Computer Applications; Computer simulation; Computer-Aided Engineering (CAD; Control; Digital media; Engineering; Information dissemination; Mechatronics; Modelling; Network topologies; Propagation; Research Article; Robotics; Simulation; Social networks; User behavior; information overload; simulating; Social network; propagation background; modeling
• ISSN: 1476-8186; 2153-182X; 1751-8520; 2153-1838
• DOI: 10.1007/s11633-020-1227-2

Recent years have witnessed the booming of online social network and social media platforms, which leads to a state of information explosion. Though extensive efforts have been made by publishers to struggle for the limited attention of audiences, still, only a few of information items will be received and digested. Therefore, for simulating the information propagation process, competition among propagating items should be considered, which has been largely ignored by prior works on propagation modeling. One possible reason may be that, it is almost impossible to identify the influence of propagation background from real diffusion data. To that end, in this paper, we design a comprehensive framework to simulate the propagation process with the characteristics of user behaviors and network topology. Specifically, we propose a propagation background simulating (PBS) algorithm to simulate the propagation background by using users′ behavior dynamics and out-degree. Along this line, an IC PB (independent cascade with propagation background) model is adapted to relieve the impact of propagation background by using users′ in-degree. Extensive experiments on kinds of synthetic and real networks have demonstrated the effectiveness of our methods.