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DESCRIPTION: Network centrality measures indicate the importance of nodes (
 or edges) in a network. In this talk we will discuss a few popular measures
  and algorithms for computing complete or partial node rankings based on th
 ese measures. These algorithms are implemented in NetworKit\, an open-sourc
 e framework for large-scale network analysis\, on which we provide an overv
 iew\, too.    One focus of the talk will be on techniques for speeding up a
  greedy (1-1/e)-approximation algorithm for the NP-hard group closeness cen
 trality problem.  Compared to a straightforward implementation\, our approa
 ch is orders of magnitude faster and\, compared to a heuristic proposed by 
 Chen et al.\, we always find a solution with better quality in a comparable
  running time in our experiments. Our  method Greedy++ allows us to approxi
 mate the group with maximum closeness centrality on networks with up to hun
 dreds of millions of edges in minutes or at most a few hours.  In a compari
 son with the optimum\, our experiments show that the solution found by Gree
 dy++ is actually much better than the theoretical guarantee. 
DTSTAMP:20181105T173400
DTSTART:20181105T141500
CLASS:PUBLIC
LOCATION:Humboldt-Universität zu Berlin\n Institut für Informatik\n Room 3.
 408 (House 3 / 4th Floor [British Reading])\n Johann von Neumann-Haus\n Rud
 ower Chaussee 25\n 12489 Berlin
SEQUENCE:0
SUMMARY:Henning Meyerhenke (Humboldt-Universität zu Berlin): Algorithms for
  Large-scale Network Analysis
UID:94302207@/www.mi.fu-berlin.de
URL:https://www.mi.fu-berlin.de/en/facetsofcomplexity/monday/20181105-L-Mey
 erhenke.html
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