Morpho-statistical description of networks through graph modelling and Bayesian inference
| Autor: | Quentin Laporte-Chabasse, Radu S. Stoica, Marianne Clausel, Francois Charoy, Gerald Oster |
|---|---|
| Přispěvatelé: | Web Scale Trustworthy Collaborative Service Systems (COAST), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Institut Élie Cartan de Lorraine (IECL), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Processus aléatoires spatio-temporels et leurs applications (PASTA), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Élie Cartan de Lorraine (IECL), Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mathematical models
Computer Networks and Communications Social networking (online) Adaptation models Collaboration Bayes methods [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] Computer Science Applications Control and Systems Engineering [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Heuristic algorithms Inference algorithms [INFO]Computer Science [cs] [MATH]Mathematics [math] |
| Zdroj: | IEEE Transactions on Network Science and Engineering IEEE Transactions on Network Science and Engineering, 2022, 9 (4), pp.2123-2138. ⟨10.1109/TNSE.2022.3155359⟩ |
| ISSN: | 2327-4697 |
| DOI: | 10.1109/TNSE.2022.3155359⟩ |
| Popis: | International audience; Collaboration graphs are relevant sources of information to understand behavioural tendencies of groups of individuals. The study of these graphs enables figuring out factors that may affect the efficiency and the sustainability of cooperative work. For example, such a collaboration involves researchers who develop relationships with their external counterparts to address scientific challenges. As relations and projects change over time, the evolution of social structures must be tackled. We propose a statistical approach considering different structural collaboration patterns and captures the dynamic of the relational structures over the years. Our approach combines spatial processes modelling and Exponential Random Graph Models used to analyse social processes. Since the normalising constant involved in classical Markov Chain Monte Carlo (MCMC) approaches is intractable, the inference remains challenging. To overcome this issue, we propose a Bayesian tool that relies on the recent ABC Shadow algorithm. The method is illustrated on real data sets from an open archive of scholarly documents. Through a simple formalism, our approach highlights the interactions between the different types of social relations at stake in the collaboration network. |
| Databáze: | OpenAIRE |
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