Factorial graphical models for dynamic networks
Bibliographic Data
| ID | 6161560 |
|---|---|
| Authors | Ernst C Wit (0000-0002-3671-9610, University of Groningen), Ernst Wit, Antonino Abbruzzo (0000-0003-2196-3570, University of Palermo) |
| Year | 2015 |
| Volume | 3 |
| Issue | 1 |
| Pages | 37-57 |
| Publication date | 2015-02-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2015.2 |
| OpenAlex | W2326532020 |
| Language | EN |
| References cited | 20 |
Dynamic network models describe many important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molecular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Until now, the literature has focused on static networks, which lack specific temporal interpretations. We propose a flexible collection of ANOVA-like dynamic network models, where the user can select specific time dynamics, known presence or absence of links, and a particular autoregressive structure. We use undirected graphical models with block equality constraints on the parameters. This reduces the number of parameters, increases the accuracy of the estimates and makes interpretation of the results more relevant. We illustrate the flexibility of the method on both synthetic and real data
Autoregressive model · Data mining · Dynamic network analysis · Econometrics · Flexibility (engineering · Graphical model · Machine learning · Process (computing · Statistics · Bioinformatics and Genomic Networks · Computer Science · Gene Regulatory Network Analysis · Mathematics · Mental Health Research Topics · Artificial Intelligence · Theoretical Computer Science
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| Highly cited | No |
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