Statistics and Its Interface

Volume 14 (2021)

Number 2

Information diffusion with network structures

Pages: 115 – 129

DOI: https://dx.doi.org/10.4310/20-SII619

Authors

Zhu Xuening (School of Data Science, Fudan-Xinzailing Joint Research Centre for Big Data, Fudan University, Shanghai, )

Rui Pan (School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China)

Yuxuan Zhang (Department of Statistics, University of California, Davis, Calif., U.S.A.)

Yu Chen (Guanghua School of Management, Peking University, Beijing, China)

Wenquan Mi (School of Data Science, Fudan-Xinzailing Joint Research Centre for Big Data, Fudan University, Shanghai, China)

Hansheng Wang (Guanghua School of Management, Peking University, Beijing, China)

Abstract

Information diffusion refers to the process about passing certain information from one subject to another. It is a typical and critical phenomenon observed in large scale social networks. To statistically model such a phenomenon, a network diffusion model is proposed and studied. The diffusion process is then investigated under the modeling framework from both the short term and long term perspectives. To estimate the model, a maximum likelihood estimator and a moment estimator are proposed, whose asymptotic properties are further established. The resulting estimators are manifested to have a reliable finite sample performance through a number of numerical studies. Lastly, the diffusion of earthquake news on Sina Weibo is analyzed to illustrate the practical usefulness.

Keywords

diffusion process, maximum likelihood estimator, moment estimator, social network

Full Text (PDF format)

Xuening Zhu is supported by the National Natural Science Foundation of China (NSFC, 11901105, 71991472, U1811461), the Shanghai Sailing Program for Youth Science and Technology Excellence (19YF1402700), and the Fudan-Xinzailing Joint Research Centre for Big Data, School of Data Science, Fudan University.

The research of Rui Pan is supported by National Natural Science Foundation of China (NSFC, 11971504, 11631003, 71771224), the Fundamental Research Funds for the Central Universities (QL18010), the Youth Talent Development Support Program (QYP1911) and the Program for Innovation Research in Central University of Finance and Economics.

Hansheng Wang’s research is partially supported by National Natural Science Foundation of China (NSFC, 11831008, 11525101, 71532001). It is also supported in part by China’s National Key Research Special Program (No. 2016YFC0207704).

Received 1 July 2019

Accepted 30 April 2020

Published 22 December 2020