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Analyzing Information Cascading in Large Scale Networks: A Fixed Point Approach

文献类型：期刊论文

作者Fu, Luoyi4; Xu, Jiasheng3; Zhou, Lei2; Wang, Xinbing3; Zhou, Chenghu1
刊名IEEE TRANSACTIONS ON MOBILE COMPUTING
出版日期2024-10-01
卷号23期号:10页码:10060-10076
关键词Stochastic processes Integrated circuit modeling Epidemics Mathematical models Analytical models Mobile computing Peer-to-peer computing Fixed point giant component information cascading random network
ISSN号1536-1233
DOI10.1109/TMC.2024.3373622
产权排序4
英文摘要Information cascading, referred as the phenomenon of an individual following the behavior of the preceding individual after observing its actions, is prevalent in real social networks and triggers intense research interests for the purpose of monitoring and controlling network epidemics. One of the typical lines of information cascading study belongs to the Influence Maximization Problem, which aims to algorithmically find the optimal seeds that can spread the information to the maximum number of nodes. Regardless of the tremendous efforts made in various algorithm design of finding such optimal seeds, it has not yet been well understood how the absolute influence power of the "optimal" source set affects the ultimate cascading, i.e., under which conditions the seeds are able or unable to influence an substantial fraction of the entire network. Most existing works have investigated the conditions of network scale influence under linear threshold model, where the activation of a node requires a large number of infected neighbors. Instead, in this paper we focus on the case of single source cascading, which is only possible to occur under the independent cascading model. We launch information cascading analysis from two aspects, i.e., the influence scale and network-scale cascading probability. Firstly, percolation analysis of the cascading outcome shows that estimating influence scale is equivalent to solving fixed point equations. Then, we investigate the speed and stability of information cascading based on fixed point analysis, which shows that the information cascading process almost surely terminates within logarithmic time complexity. Furthermore, the results are generalized to the stochastic block model, where we find that network-scale cascading is determined by the spectral radius of the community matrix. The analysis presented in this paper could help us better understand the conditions for different information cascading outcomes.
WOS关键词INFLUENCE MAXIMIZATION
资助项目NSF[62020106005] ; NSF[61960206002] ; NSF[42050105] ; NSF[62061146002] ; Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:001306818600006
出版者IEEE COMPUTER SOC
资助机构NSF ; Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University
源URL[http://ir.igsnrr.ac.cn/handle/311030/208683]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Wang, Xinbing
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100045, Peoples R China
2.Shanghai Jiao Tong Univ, Sch Oceanog, Shanghai 200240, Peoples R China
3.Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China
4.Shanghai Jiao Tong Univ, Dept Comp Sci, Engn, Shanghai 200240, Peoples R China
推荐引用方式
GB/T 7714		 Fu, Luoyi,Xu, Jiasheng,Zhou, Lei,et al. Analyzing Information Cascading in Large Scale Networks: A Fixed Point Approach[J]. IEEE TRANSACTIONS ON MOBILE COMPUTING,2024,23(10):10060-10076.
APA Fu, Luoyi,Xu, Jiasheng,Zhou, Lei,Wang, Xinbing,&Zhou, Chenghu.(2024).Analyzing Information Cascading in Large Scale Networks: A Fixed Point Approach.IEEE TRANSACTIONS ON MOBILE COMPUTING,23(10),10060-10076.
MLA Fu, Luoyi,et al."Analyzing Information Cascading in Large Scale Networks: A Fixed Point Approach".IEEE TRANSACTIONS ON MOBILE COMPUTING 23.10(2024):10060-10076.

入库方式： OAI收割

来源：地理科学与资源研究所

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