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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.04622 |
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| _version_ | 1866916676107239424 |
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| author | Xing, Zhaoyu Wang, Y. X. Rachel Wood, Andrew T. A. Zou, Tao |
| author_facet | Xing, Zhaoyu Wang, Y. X. Rachel Wood, Andrew T. A. Zou, Tao |
| contents | This article introduces a regularization and selection methods for directed networks with nodal homophily and nodal effects. The proposed approach not only preserves the statistical efficiency of the resulting estimator, but also ensures that the selection of nodal homophily and nodal effects is scalable with large-scale network data and multiple nodal features. In particular, we propose a directed random network model with nodal homophily and nodal effects, which includes the nodal features in the probability density of random networks. Subsequently, we propose a regularized maximum likelihood estimator with an adaptive LASSO-type regularizer. We demonstrate that the regularized estimator exhibits the consistency and possesses the oracle properties. In addition, we propose a network Bayesian information criterion which ensures the selection consistency while tuning the model. Simulation experiments are conducted to demonstrate the excellent numerical performance. An online friendship network among musicians with nodal musical preference is used to illustrate the usefulness of the proposed new network model in network-related empirical analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regularization and Selection in A Directed Network Model with Nodal Homophily and Nodal Effects Xing, Zhaoyu Wang, Y. X. Rachel Wood, Andrew T. A. Zou, Tao Methodology 62J07, 62F12, 05C82 This article introduces a regularization and selection methods for directed networks with nodal homophily and nodal effects. The proposed approach not only preserves the statistical efficiency of the resulting estimator, but also ensures that the selection of nodal homophily and nodal effects is scalable with large-scale network data and multiple nodal features. In particular, we propose a directed random network model with nodal homophily and nodal effects, which includes the nodal features in the probability density of random networks. Subsequently, we propose a regularized maximum likelihood estimator with an adaptive LASSO-type regularizer. We demonstrate that the regularized estimator exhibits the consistency and possesses the oracle properties. In addition, we propose a network Bayesian information criterion which ensures the selection consistency while tuning the model. Simulation experiments are conducted to demonstrate the excellent numerical performance. An online friendship network among musicians with nodal musical preference is used to illustrate the usefulness of the proposed new network model in network-related empirical analysis. |
| title | Regularization and Selection in A Directed Network Model with Nodal Homophily and Nodal Effects |
| topic | Methodology 62J07, 62F12, 05C82 |
| url | https://arxiv.org/abs/2504.04622 |