🎞 Here's a video of Timothy Gowers' talk, gave in Tromso a few weeks ago about the state of academic publishing, and about why a system whose flaws are obvious to almost everybody is as robust as it is.
https://mediasite.uit.no/Mediasite/Play/db5614d2d8de4b528b62929b5209355d1d?PlayFrom=2400000&_utm_source=1-2-2
https://mediasite.uit.no/Mediasite/Play/db5614d2d8de4b528b62929b5209355d1d?PlayFrom=2400000&_utm_source=1-2-2
Forwarded from Deleted Account [SCAM]
Media is too big
VIEW IN TELEGRAM
Why do animals form swarms? - Maria R. D'Orsogna
🔖 Different approaches to community detection
Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, Renaud Lambiotte
🔗 arxiv.org/pdf/1712.06468.pdf
📌 ABSTRACT
A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.
Martin Rosvall, Jean-Charles Delvenne, Michael T. Schaub, Renaud Lambiotte
🔗 arxiv.org/pdf/1712.06468.pdf
📌 ABSTRACT
A precise definition of what constitutes a community in networks has remained elusive. Consequently, network scientists have compared community detection algorithms on benchmark networks with a particular form of community structure and classified them based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different reasons for why we would want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different approaches to community detection also delineates the many lines of research and points out open directions and avenues for future research.
🎞 Stanford Complexity Symposium Videos Now Online
https://www.youtube.com/playlist?list=PL4K77C_Lzqan0Ysv_BtrTDJT8vAt2ezC7
https://www.youtube.com/playlist?list=PL4K77C_Lzqan0Ysv_BtrTDJT8vAt2ezC7
YouTube
Stanford Complexity Symposium - 11/14/2017 - YouTube
🎞 How can we understand a city through its infrastructure networks?
https://cns.ceu.edu/article/2017-12-19/visualizing-street-network-budapest
https://vimeo.com/247808832?ref=tw-share
https://cns.ceu.edu/article/2017-12-19/visualizing-street-network-budapest
https://vimeo.com/247808832?ref=tw-share
Vimeo
Budapest Network Attack Tolerance
Simulation of a network attack based on the betweenness centrality of the nodes.
Complex Systems Studies
https://iasbs.ac.ir/seminar/physics/condmat-meeting/m24/
A one-day school is also planned prior to the meeting, which will offer lectures for graduate students and young researchers on a special topic each year. The subject of this year's school will be "Complex Systems".
A Peregrine falcon chasing a flock of starling. Awesome pics by Nick Dunlop https://t.co/Tdvn4pOMWs