Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Parameterizing the distance distribution of undirected networks
 Meila, M. ; Association for the Advancement of Artificial Intelligence AAAI; Association for Uncertainty in Artificial Intelligence AUAI: 31st Conference on Uncertainty in Artificial Intelligence 2015 : Amsterdam, Netherlands, 12  16 July 2015 Red Hook, NY: Curran, 2015 ISBN: 9781510810860 S.121130 
 Conference on Uncertainty in Artificial Intelligence (UAI) <31, 2015, Amsterdam> Advances in Causal Inference Workshop <2015, Amsterdam> 

 Englisch 
 Konferenzbeitrag, Elektronische Publikation 
 Fraunhofer IAIS () 
Abstract
Network statistics such as node degree distributions, average path lengths, diameters, or clustering coefficients are widely used to characterize networks. One statistic that received considerable attention is the distance distribution  the number of pairs of nodes for each shortestpath distance  in undirected networks. It captures important properties of the network, reflecting on the dynamics of network spreading processes, and incorporates parameters such as node centrality and (effective) diameter. So far, however, no parameterization of the distance distribution is known that applies to a large class of networks. Here we develop such a closedform distribution by applying maximum entropy arguments to derive a general, physically plausible model of path length histograms. Based on the model, we then establish the generalized Gamma as a threeparameter distribution for shortestpath distance in stronglyconnected, undirected networks. Extensive experiments corroborate our theoretical results, which thus provide new approaches to network analysis.