Databeers Dub #6 - Alberto Caimo - Investigating the local dependencies of network data, the Bayesian way
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Investigating the local dependencies of network data, the Bayesian way
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Databeers Dub #6 - Alberto Caimo - Investigating the local dependencies of network data, the Bayesian way
- 1. Investigating the local dependencies of network data, the Bayesian way Alberto Caimo acaimo.github.io New Relic October 3, 2018
- 3. Network graph Adjacency matrix Network data
- 4. Whole-brain network Modelling brain networks
- 5. Identify how local network structures are evolving across the lifespan, and how sensitive these are to random / targeted lesions Whole-brain network Research objective Modelling brain networks
- 6. Identify how local network structures are evolving across the lifespan, and how sensitive these are to random / targeted lesions Whole-brain network Research objective Statistical network analysis Modelling brain networks
- 7. Graph changes across lifespan ng percentages of nodes with the highest betweenness centrality. uated the consequences of random-node and hub-node damage ocal structure parameters (e.g. edges, GWESP, GWNSP, and hemi- nodematch). The effect of simulated damage between percentages ated nodes was quantified with 95% credibility intervals obtained difference between the posterior distributions. 3. Results 3.1. Network matrices and model parameters We generated group-based networks for each age category are depicted in Fig. 3 as network graph representations (for Age 20-34 Age 20-34 vs Age 35-50 lost connections new connections
- 8. Probabilistic: describe the probability of the network structure Generative: try to explain how the network structure might have been generated Assumption: links formation depend on the relative presence / absence of some local network structures Exponential random graphs
- 12. Sparsity Transitivity Prior Posterior — — — + Density Multiple Closure ERGM … the Bayesian way… Multiple Connectivity
- 13. Sparsity Transitivity Prior Posterior computationally doubly-intractable! — — — + Simulation-based computation Density Multiple Closure ERGM … the Bayesian way… Multiple Connectivity
- 14. The Bergm package for On CRAN: CRAN.R-project.org/package=Bergm Website: acaimo.github.io/Bergm Bayesian Analysis Exponential Random Graph Models Missing Data Imputation Parameter InferenceBergm Model Selection Model Assessment
- 15. Results Fig. 5. Network parameters across lifespan. Edges, GWESP, GWNSP, and hemispheric nodematch for age categories 20–34, 35–50, 51–70, and N70 years of age. 86 M.R.T. Sinke et al. / NeuroImage 135 (2016) 79–91 Density Multiple Closure Multiple Connectivity Hemispheric Density Age groups Age groups Structural networks remain stable across the lifespan
- 16. Results organization (Bullmore and Sporns, dematch values indicate a tendency (Dennis et al., 2013; Gong et al., 2009; Hagmann et al., 201 Montembeault et al., 2012; Otte et al., 2015; Wu et al., 2012; Z Density Multiple Closure Multiple Connectivity Hemispheric Density Age groups Age groups Hub-node damage has stronger effects in people above 70 years of age
- 17. BERGM: many applications! Knowledge transfer in organisations Foreign direct investment flow dynamics Sustainable energy district planning More info: acaimo.github.io