Business location planning based on a novel geo-social influence diffusion model

2021 ◽  
Vol 559 ◽  
pp. 61-74
Author(s):  
Qian Zeng ◽  
Ming Zhong ◽  
Yuanyuan Zhu ◽  
Tieyun Qian ◽  
Jianxin Li
1991 ◽  
Vol 68 (3_suppl) ◽  
pp. 1185-1186 ◽  
Author(s):  
Richard H. Evans

This research examined a diffusion model that included normative social influence. Findings were based on responses of 137 undergraduate business school students who served as subjects and examined the product, athletic shoes, and indicate that normative social influence may be included in the diffusion model by using Newton's Method to provide a good fit with the data.


2020 ◽  
Vol 64 (1) ◽  
pp. 345-358
Author(s):  
Senbo Chen ◽  
Wenan Tan

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Joseph J. Nalluri ◽  
Pratip Rana ◽  
Debmalya Barh ◽  
Vasco Azevedo ◽  
Thang N. Dinh ◽  
...  

2015 ◽  
Vol 719-720 ◽  
pp. 886-896
Author(s):  
Georges Olle Olle ◽  
Shu Qin Cai ◽  
Qian Yuan ◽  
Shi Miao Jiang

Customer Churn is a pesky problem that continues to haunt telecommunication companies. Social influence analysis has recently been introduced in churn prediction, motivated by the fact thatsome users can churn due to accumulated churn influence that they’ve received from other churners.We’ve collected call data records of about 10 thousands mobile phone users of one the largest mobile network operators in China,and have built a Multi-relational call network which is a graph constructed by mobile phone users considered as nodes and the interactive calls between them considered as the relationships or edges. We’veapplied Linear Threshold (LT) to model the diffusion of churners influence in theobtainedsocial network, in order to analyze the relevance of social affinities in the diffusion of churn influence.The results indicate that churn influence diffusion depends not only on the number of initial churners but also on the existingaffinities between users.


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