Analysis of weighted networks Export

@article{citeulike:281, abstract = {The connections in many networks are not merely binary entities, eitherpresent or not, but have associated weights that record their strengthsrelative to one another. Recent studies of networks have, by and large, steeredclear of such weighted networks, which are often perceived as being harder toanalyze than their unweighted counterparts. Here we point out that weightednetworks can in many cases be analyzed using a simple mapping from a weightednetwork to an unweighted multigraph, allowing us to apply standard techniquesfor unweighted graphs to weighted ones as well. We give a number of examples ofthe method, including an algorithm for detecting community structure inweighted networks and a new and simple proof of the max-flow/min-cut theorem.}, archivePrefix = {arXiv}, author = {Newman, M. E. J.}, citeulike-article-id = {281}, citeulike-linkout-0 = {http://arxiv.org/abs/cond-mat/0407503}, citeulike-linkout-1 = {http://arxiv.org/pdf/cond-mat/0407503}, day = {20}, eprint = {cond-mat/0407503}, keywords = {community-discovery, graph, weighted}, month = {Jul}, posted-at = {2009-10-10 11:23:10}, priority = {2}, title = {Analysis of weighted networks}, url = {http://arxiv.org/abs/cond-mat/0407503}, year = {2004} }

TY - JOUR ID - citeulike:281 L3 - citeulike-article-id:281 N2 - The connections in many networks are not merely binary entities, eitherpresent or not, but have associated weights that record their strengthsrelative to one another. Recent studies of networks have, by and large, steeredclear of such weighted networks, which are often perceived as being harder toanalyze than their unweighted counterparts. Here we point out that weightednetworks can in many cases be analyzed using a simple mapping from a weightednetwork to an unweighted multigraph, allowing us to apply standard techniquesfor unweighted graphs to weighted ones as well. We give a number of examples ofthe method, including an algorithm for detecting community structure inweighted networks and a new and simple proof of the max-flow/min-cut theorem. TI - Analysis of weighted networks KW - community-discovery KW - graph KW - weighted AU - Newman, MEJ PY - 2004/Jul/20/ UR - http://arxiv.org/abs/cond-mat/0407503 ER -