BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Neighbourhood matching creates realistic surrogate temporal networ
 ks - Antonio Longa\, FBK
DTSTART:20220620T160000Z
DTEND:20220620T170000Z
UID:TALK175934@talks.cam.ac.uk
CONTACT:Pietro Lio
DESCRIPTION:Temporal networks are essential for modeling and understanding
  systems whose behavior varies in time\, from social interactions to biolo
 gical systems.\nOften\, however\, real-world data are prohibitively expens
 ive to collect or unshareable due to privacy concerns. A promising solutio
 n is ‘surrogate networks’\, synthetic graphs with the properties of re
 al-world networks. Until now\, the generation of realistic surrogate tempo
 ral networks has remained an open problem\, due to the difficulty of captu
 ring both the temporal and topological properties of the input network\, a
 s well as their correlations\, in a scalable model. Here\, we propose a no
 vel and simple method for generating surrogate temporal networks. By decom
 posing\ngraphs into temporal neighborhoods surrounding each node\, we can 
 generate new networks using neighborhoods as building blocks. Our model va
 stly outperforms\ncurrent methods across multiple examples of temporal net
 works in terms of both topological and dynamical similarity. We further sh
 ow that beyond generating realistic interaction patterns\, our method is a
 ble to capture intrinsic temporal periodicity\nof temporal networks\, all 
 with an execution time lower than competing methods by multiple orders of 
 magnitude.
LOCATION:Lecture theatre and zoom (https://cl-cam-ac-uk.zoom.us/j/97150757
 190?pwd=Q2Nib09aVi9CRFptSFY3NlZGUnhUdz09)
END:VEVENT
END:VCALENDAR