Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 145-157 |
| Number of pages | 13 |
| Journal | Journal of Econometrics |
| Volume | 209 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 1 2019 |
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All Science Journal Classification (ASJC) codes
- Economics and Econometrics
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Portal nodes screening for large scale social networks. / Zhu, Xuening; Chang, Xiangyu; Li, Runze; Wang, Hansheng.
In: Journal of Econometrics, Vol. 209, No. 2, 01.04.2019, p. 145-157.Research output: Contribution to journal › Article
TY - JOUR
T1 - Portal nodes screening for large scale social networks
AU - Zhu, Xuening
AU - Chang, Xiangyu
AU - Li, Runze
AU - Wang, Hansheng
PY - 2019/4/1
Y1 - 2019/4/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85060332580&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060332580&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2018.12.021
DO - 10.1016/j.jeconom.2018.12.021
M3 - Article
AN - SCOPUS:85060332580
VL - 209
SP - 145
EP - 157
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -