User Tools

Site Tools


ev:2017:pdm_program

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
ev:2017:pdm_program [2017/10/20 17:06]
konstantin
ev:2017:pdm_program [2017/10/26 08:48] (current)
konstantin
Line 18: Line 18:
 | 13:00  | lunch break       || | 13:00  | lunch break       ||
 | // Working Group 1: Exploring (massive) network data sets // ||| | // Working Group 1: Exploring (massive) network data sets // |||
-| 14:00  | see speakers ​and titles below, schedule ​to be announced by working group chair|| +| 14:00  | Di Serio | Welcome to the session | 
-| 16:00  | coffee break ||+| 14:10  | Subelj ​  | Three forms of convexity in graphs ​and networks| 
 +| 14:30  | Cortes | From multimodal images ​to connectivity of brain networks in both healthy and pathological conditions| 
 +| 14:50  | Martincic | The Multilayer Networks for Language| 
 +| 15:10  | Cugmas | The emergence of core-cohesive peripheries blockmodel type| 
 +| 15:30  | Drejerska | Network analysis of commuting flows for delimitation of functional urban areas| 
 +| 15:50  | Chrzanowska ​Network Analysis of Commuting Flows in Poland
 +| 16:10  | coffee break ||
 | // Action Conference: Posters // ||| | // Action Conference: Posters // |||
-| 16:30  | see list below    | flash talks advertising posters |+| 16:40  | see list below    | flash talks advertising posters |
 | 17:00  | see  list below   | poster session | | 17:00  | see  list below   | poster session |
  
Line 42: Line 48:
 | 12:26 | Kadilar | On the Graphical Markov Models with an Application| | 12:26 | Kadilar | On the Graphical Markov Models with an Application|
 | 12:43 | Michaud | A stochastic heterogeneous mean-field approximation of agent-based models| | 12:43 | Michaud | A stochastic heterogeneous mean-field approximation of agent-based models|
-| 13:00 | lunch break       ||+| 13:00 | group photo (in front of the lecture hall), then lunch break       ||
 | // Working Group 3: Network Inference and Prediction // ||| | // Working Group 3: Network Inference and Prediction // |||
 | 14:00 | Elliott | A novel approach to network anomaly detection| | 14:00 | Elliott | A novel approach to network anomaly detection|
Line 50: Line 56:
 | 15:40 | Nurushev | Local inference by penalization method for biclustering model| | 15:40 | Nurushev | Local inference by penalization method for biclustering model|
 | 16:05 | coffee || | 16:05 | coffee ||
 +| 16:15 | bus departure for old town of Palma ||
  
 ==== Friday (Oct 27) ==== ==== Friday (Oct 27) ====
  
 ^ time ^ presenting author ^ title ^ ^ time ^ presenting author ^ title ^
-| 09:00 | Lupparelli | Regression graph models for binary ​non-independent ​ ​outcomes ​|+| 09:00 | Lupparelli | Graphical ​models for sequences of non-independent ​regressions ​|
 | 09:35 | Ibañez-Marcelo | When shape matters: Brain networks studied under a persistent homology view| | 09:35 | Ibañez-Marcelo | When shape matters: Brain networks studied under a persistent homology view|
 | 10:00 | Fernandez-Gracia | Gromov-Wasserstein distance of complex networks| | 10:00 | Fernandez-Gracia | Gromov-Wasserstein distance of complex networks|
 | 10:25 | Batagelj | Describing network evolution using probabilistic inductive classes| | 10:25 | Batagelj | Describing network evolution using probabilistic inductive classes|
-| 10:35 | coffee break || +| 10:50 | coffee break || 
-| 11:05 | Rancoita | Bayesian networks for data imputation in survival tree analysis| +| 11:20 | Rancoita | Bayesian networks for data imputation in survival tree analysis| 
-| 11:30 | Signorelli |How to integrate gene enrichment analysis with information from gene interaction networks| +| 11:45 | Signorelli |How to integrate gene enrichment analysis with information from gene interaction networks| 
-11:55 | Stadler | Gene Trees, Species Trees, Reconciliation Maps, and Phylogenenies| +12:10 | Stadler | Gene Trees, Species Trees, Reconciliation Maps, and Phylogenenies| 
-| 12:30 | closing, lunch |+| 12:45 | closing, lunch ||
- +
- +
-==== Meeting of working group 1: Exploring (massive) network data sets ==== +
- +
-^ presenting author ^ title ^ +
-|Chrzanowska | Network Analysis of Commuting Flows in Poland| +
-|Cortes | From multimodal images to connectivity of brain networks in both healthy and pathological conditions| +
-|Cugmas | The emergence of core-cohesive peripheries blockmodel type| +
-|Drejerska | Network analysis of commuting flows for delimitation of functional urban areas| +
-|Fierascu | Corruption Networks in Post-Communist Countries. The Case of State Capture in Hungary| +
-|Martincic | The Multilayer Networks for Language| +
-|Subelj | Three forms of convexity in graphs and networks|+
  
  
Line 93: Line 88:
 |//Lee//| A Network Epidemic Model for Online Community Commissioning Data | |//Lee//| A Network Epidemic Model for Online Community Commissioning Data |
 |Lehmann | ERGMs in neuroimaging - developments and challenges| |Lehmann | ERGMs in neuroimaging - developments and challenges|
-|Leskela | Moment-based parameter estimation in binomial random intersection graph |+|//Leskela//| Moment-based parameter estimation in binomial random intersection graph |
 |Marchetti | Graphoid properties and independence with Credal Networks | |Marchetti | Graphoid properties and independence with Credal Networks |
 |Min | Fragmentation transitions in a coevolving nonlinear voter model | |Min | Fragmentation transitions in a coevolving nonlinear voter model |
ev/2017/pdm_program.1508512008.txt.gz · Last modified: 2017/10/20 17:06 by konstantin