The 2019 edition of Netmob is approaching! Submit you abstracts and let's meet in Oxford Mathematics !

This will be a great thing!

Due to a number of requests, we have decided to extend the deadline for abstract submission. The updated and final deadline is Monday March 25!

Awesome, network analysis GitHub - briatte/awesome-network-analysis: A curated list of awesome network analysis resources. github.com/briatte/awesom…

How to get more reliable and accurate traffic models from mobile phone data by Klaas Friso @netmob17

Understanding short term mobility in Senegal and Namibia by Svetoslava Milusheva @netmob17

Mobile internet usage to understand mobility activity patterns by Yihong Wang @netmob17

The effect of #PokemonGO on the pulse of the city by Edward Carnby Hardy Gains how to use mobile phone data to understand the effect of new games/apps

Traffic analysis of railways using mobile phone data by Hitoshi Ishizuka of KDDI総合研究所 広報 at @netmob17

Detecting evacuation hotspots after a big earthquake with mobile phone data @netmob17 by Takahiro Yabe

Mapping poverty using mobile phone and satellite data by Jessica Steele @netmob17

Describing land-use in areas using event time series from mobile phone data by Gregor Engelmann @netmob17

Containing disease spreading using spatial network analysis, presented by Stefania Rubrichi @netmob17

"Who calls whom, when & where has a lot applications", Carolina Mattsson on what mobile phone data tells about sudden emergencies @netmob17

Predicting device upgrade using data science by Roberto Caporici Vodafone it now at @netmob17

Mobile phone datasets help us to better understand human behavior. @netmob17 #Vodafone #BigData

Layers is social interactions for churn prediction presented by Davide Cellai at @netmob17

Final talk at @netmob17 Daniel Daniel Björkegren on how people learn about new technologies using evidence from phone plans in Rwanda

Thank you all for this great edition of Netmob in Milano! Thanks to Francesco Calabrese and the Vodafone it team for the organization!

This is very interesting - Federated Learning: Collaborative Machine Learning without Centralized Training Data research.googleblog.com/2017/04/federa…