TY - JOUR
T1 - Portal nodes screening for large scale social networks
AU - Zhu, Xuening
AU - Chang, Xiangyu
AU - Li, Runze
AU - Wang, Hansheng
N1 - Funding Information:
The research of Zhu and Li was supported by NIDA, NIH grants P50 DA039838, National Library of Medicine, T32 LM012415, National Institute of Allergy and Infectious Diseases, U19AI089672 and National Science Foundation grant, DMS 1820702, and National Nature Science Foundation of China (NNSFC)11690015. This work was also partially supported by National Natural Science Foundation of China grant U1811461. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NSF, the NIDA, the NIH, or the NNSFC. Chang's research was supported in part by NNSFC11771012, 91546119 and 71472023. The authors are very grateful to the Editor, the Associate Editor and two reviewers for their constructive comments, which leads to a significant improvement of this work.
Publisher Copyright:
© 2018
PY - 2019/4
Y1 - 2019/4
N2 - Network autoregression model (NAM), as a powerful tool to study user social behaviors on large scale social networks, has drawn great attention in recent years. In this paper, we are interested in identifying the influential users (i.e., portal nodes) in a social network under the framework of NAM. Especially, we consider the autoregression model that allows to have a heterogeneous and sparse network effect coefficients. Therefore, the portal nodes take influential powers which are corresponding to the nonzero network effect coefficients. A screening procedure is designed to screen out the portal nodes and the strong screening consistency is established theoretically. A quasi maximum likelihood method is applied to estimate the influential powers. The asymptotic normality of the resulting estimator is established. Further selection procedure is given by taking advantage of the local linear approximation algorithm. Extensive numerical studies are conducted by using a Sina Weibo dataset for illustration purpose.
AB - Network autoregression model (NAM), as a powerful tool to study user social behaviors on large scale social networks, has drawn great attention in recent years. In this paper, we are interested in identifying the influential users (i.e., portal nodes) in a social network under the framework of NAM. Especially, we consider the autoregression model that allows to have a heterogeneous and sparse network effect coefficients. Therefore, the portal nodes take influential powers which are corresponding to the nonzero network effect coefficients. A screening procedure is designed to screen out the portal nodes and the strong screening consistency is established theoretically. A quasi maximum likelihood method is applied to estimate the influential powers. The asymptotic normality of the resulting estimator is established. Further selection procedure is given by taking advantage of the local linear approximation algorithm. Extensive numerical studies are conducted by using a Sina Weibo dataset for illustration purpose.
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U2 - 10.1016/j.jeconom.2018.12.021
DO - 10.1016/j.jeconom.2018.12.021
M3 - Article
C2 - 31798203
AN - SCOPUS:85060332580
VL - 209
SP - 145
EP - 157
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -