arXiv:1409.4813v1 [cs.SI] 16 Sep 2014 Identification of core-periphery structure in networks Xiao Zhang,1 Travis Martin,2 and M. E. J. Newman1, 3 1 Department of Physics, University of Michigan, Ann Arbor, MI 48109 2 Department of Electrical Engineering and...
More
arXiv:1409.4813v1 [cs.SI] 16 Sep 2014 Identification of core-periphery structure in networks Xiao Zhang,1 Travis Martin,2 and M. E. J. Newman1, 3 1 Department of Physics, University of Michigan, Ann Arbor, MI 48109 2 Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 3 Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109 Many networks can be usefully decomposed into a dense core plus an outlying, loosely-connected periphery. Here we propose an algorithm for performing such a decomposition on empirical network data using methods of statistical inference. Our method fits a generative model of core–periphery structure to observed data using a combination of an expectation–maximization algorithm for cal- culating the parameters of the model and a belief propagation algorithm for calculating the decom- position itself. We find the method to be efficient, scaling easily to networks with a million or more node
Less
