Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model
Everything is connected in the world. From small groups to global societies, the interactions among people, technology, and policies need sophisticated techniques to be perceived and forecasted. In social network, it has been concluded that the microblog users influence and microblog grade are nonli...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2019/6237406 |
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| _version_ | 1832561739414110208 |
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| author | Ya-hui Jia Taotao Song Shun-yao Wu Qi Zhang Yu-xia Su |
| author_facet | Ya-hui Jia Taotao Song Shun-yao Wu Qi Zhang Yu-xia Su |
| author_sort | Ya-hui Jia |
| collection | DOAJ |
| description | Everything is connected in the world. From small groups to global societies, the interactions among people, technology, and policies need sophisticated techniques to be perceived and forecasted. In social network, it has been concluded that the microblog users influence and microblog grade are nonlinearly dependent. However, to the best of our knowledge, the nonlinear influence predication of social network has not been explored in the existing literature. This article proposes a partial autoregression single index model to combine network structure (linear) and static covariates (nonparametric) flexibly. Compared with previous work, our model has fewer limits and more applications. The profile least squares estimation is employed to infer this semiparametric model, and variables selection is performed via the smoothly clipped absolute deviation penalty (SCAD). Simulations are conducted to demonstrate finite sample behaviors. |
| format | Article |
| id | doaj-art-e1f30e9122bb49b0ac939acc066cedb5 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-e1f30e9122bb49b0ac939acc066cedb52025-02-03T01:24:27ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2019-01-01201910.1155/2019/62374066237406Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index ModelYa-hui Jia0Taotao Song1Shun-yao Wu2Qi Zhang3Yu-xia Su4Qingdao University, Qingdao 266071, ChinaDaqin Railway Co Ltd, ChinaQingdao University, Qingdao 266071, ChinaQingdao University, Qingdao 266071, ChinaQufu Normal University, ChinaEverything is connected in the world. From small groups to global societies, the interactions among people, technology, and policies need sophisticated techniques to be perceived and forecasted. In social network, it has been concluded that the microblog users influence and microblog grade are nonlinearly dependent. However, to the best of our knowledge, the nonlinear influence predication of social network has not been explored in the existing literature. This article proposes a partial autoregression single index model to combine network structure (linear) and static covariates (nonparametric) flexibly. Compared with previous work, our model has fewer limits and more applications. The profile least squares estimation is employed to infer this semiparametric model, and variables selection is performed via the smoothly clipped absolute deviation penalty (SCAD). Simulations are conducted to demonstrate finite sample behaviors.http://dx.doi.org/10.1155/2019/6237406 |
| spellingShingle | Ya-hui Jia Taotao Song Shun-yao Wu Qi Zhang Yu-xia Su Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model Discrete Dynamics in Nature and Society |
| title | Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model |
| title_full | Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model |
| title_fullStr | Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model |
| title_full_unstemmed | Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model |
| title_short | Dynamic Influence Prediction of Social Network Based on Partial Autoregression Single Index Model |
| title_sort | dynamic influence prediction of social network based on partial autoregression single index model |
| url | http://dx.doi.org/10.1155/2019/6237406 |
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