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SUMMARY:Machine learning for network inference - Ljupco Todorovski (Univer
 sity of Ljubljana)
DTSTART:20200211T130000Z
DTEND:20200211T140000Z
UID:TALK139423@talks.cam.ac.uk
CONTACT:Mateja Jamnik
DESCRIPTION:Complex network analysis emerges as a wide-spread analytical t
 ool in many domains of science. It assumes that we can observe both the en
 tities that represent the network nodes as well as the relations among the
 m. In numerous scenarios\, where the relations are not always immediately 
 observable\, we encounter the task of network inference. Given the observa
 tion of the dynamical change of the nodes' properties\, the task is to rec
 onstruct the unobservable links among the nodes. The talk will present thr
 ee approaches to network inference that build upon statistical measures of
  pairwise associations between nodes\, machine learning methods for featur
 e ranking\, and equation discovery methods for modeling network dynamics. 
 We will also consider brief comparative analysis of the approaches\, illus
 trate their utility on tasks of reconstructing known networks and discuss 
 the prospects for developing an integrative approach.\n\nRecommended Readi
 ngs\n* Kuzmanovski V\, Todorovski L\, Dzeroski S (2018) GigaScience 7(11):
  giy118. doi:10.1093/gigascience/giy118\n* Leguia MG\, Levnajic Z\, Todoro
 vski L\, Zenko B (2019) Chaos 29: 093107. doi:10.1063/1.5092170 also arXiv
 :1902.03896\n* Simidjievski N\, Tanevski J\, Zenko B\, Levnajic \, Todorov
 ski L\, Dzeroski S (2018) New Journal of Physics 20(11): 113003. doi:10.10
 88/1367-2630/aae941 also arXiv:1712.03100\n
LOCATION:LT1\, Computer Laboratory\, William Gates Builiding\, West Cambri
 dge site
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