Conditionally exponential random models for individual properties and network structures: Method and application |
| |
| Institution: | 1. Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King''s College London, London, UK;2. Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden;3. Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden;4. Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden;5. Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King''s College London, London, UK;6. NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia at South London & Maudsley NHS Foundation, London, UK;7. Centre for Age Related Research, Stavanger University Hospital, Stavanger, Norway;8. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden;9. Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK;10. UK Dementia Research Institute at UCL, London, UK |
| |
| Abstract: | Exponential random models have been widely adopted as a general probabilistic framework for complex networks and recently extended to embrace broader statistical settings such as dynamic networks, valued networks or two-mode networks. Our aim is to provide a further step into the generalization of this class of models by considering sample spaces which involve both families of networks and nodal properties verifying combinatorial constraints. We propose a class of probabilistic models for the joint distribution of nodal properties (demographic and behavioral characteristics) and network structures (friendship and professional partnership). It results in a general and flexible modeling framework to account for homophily in social structures. We present a Bayesian estimation method based on the full characterization of their sample spaces by systems of linear constraints. This provides an exact simulation scheme to sample from the likelihood, based on linear programming techniques. After a detailed analysis of the proposed statistical methodology, we illustrate our approach with an empirical analysis of co-authorship of journal articles in the field of neuroscience between 2009 and 2013. |
| |
| Keywords: | Exponential random models Social networks Homophily Bibliometrics Bayesian inference MCMC |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
