Learning cross-layer dependence structure in multilayer networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917887376097280 |
|---|---|
| author | Li, Jiaheng Stewart, Jonathan R. |
| author_facet | Li, Jiaheng Stewart, Jonathan R. |
| contents | We propose a novel class of separable multilayer network models to capture cross-layer dependencies in multilayer networks, enabling the analysis of how interactions in one or more layers may influence interactions in other layers. Our approach separates the network formation process from the layer formation process, and is able to extend existing single-layer network models to multilayer network models that accommodate cross-layer dependence. We establish non-asymptotic and minimax-optimal error bounds for maximum likelihood estimators and demonstrate the convergence rate in scenarios of increasing parameter dimension. Additionally, we establish non-asymptotic error bounds for multivariate normal approximations and propose a model selection method that controls the false discovery rate. Simulation studies and an application to the Lazega lawyers network show that our framework and method perform well in realistic settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_14982 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning cross-layer dependence structure in multilayer networks Li, Jiaheng Stewart, Jonathan R. Statistics Theory Methodology We propose a novel class of separable multilayer network models to capture cross-layer dependencies in multilayer networks, enabling the analysis of how interactions in one or more layers may influence interactions in other layers. Our approach separates the network formation process from the layer formation process, and is able to extend existing single-layer network models to multilayer network models that accommodate cross-layer dependence. We establish non-asymptotic and minimax-optimal error bounds for maximum likelihood estimators and demonstrate the convergence rate in scenarios of increasing parameter dimension. Additionally, we establish non-asymptotic error bounds for multivariate normal approximations and propose a model selection method that controls the false discovery rate. Simulation studies and an application to the Lazega lawyers network show that our framework and method perform well in realistic settings. |
| title | Learning cross-layer dependence structure in multilayer networks |
| topic | Statistics Theory Methodology |
| url | https://arxiv.org/abs/2307.14982 |