Hurley, Neil J.Neil J.HurleyDuriakova, ErikaErikaDuriakova2017-03-292017-03-292015 IEEE2015-08-289781450338547http://hdl.handle.net/10197/8413The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015), Paris, France, 25-28 August 2015Among the many community-finding algorithms that have been proposed in the last decade and more, the Infomapalgorithm of Rosvall and Bergstrom has proven among the best. The algorithm finds good community structure in directed aswell as undirected networks by abstracting information flow inthe network as a random walk. In this paper, we reformulate the objective in terms of the Kullback-Leibler distance between thedistribution of the random walk transitions and that of a modelwalk. The choice of model can be used to constrain the typeof partition that the method extracts. This generalisation makesthe method suitable for extracting other types of meso-structurefrom the network, enabling the analyst to explicitly control thetype of extracted structure.en© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Machine learningStatisticsInfomap methodGeneralised blockmodellingConference Publication10.1145/2808797.28093562016-11-17https://creativecommons.org/licenses/by-nc-nd/3.0/ie/