doi abstract bibtex

The connections in many networks are not merely binary entities, either present or not, but have associated weights that record their strengths relative to one another. Recent studies of networks have, by and large, steered clear of such weighted networks, which are often perceived as being harder to analyze than their unweighted counterparts. Here we point out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighted graphs to weighted ones as well. We give a number of examples of the method, including an algorithm for detecting community structure in weighted networks and a simple proof of the maximum-flow-minimum-cut theorem.

@article{newmanAnalysisWeightedNetworks2004, title = {Analysis of Weighted Networks}, volume = {70}, issn = {15393755}, doi = {10.1103/PhysRevE.70.056131}, abstract = {The connections in many networks are not merely binary entities, either present or not, but have associated weights that record their strengths relative to one another. Recent studies of networks have, by and large, steered clear of such weighted networks, which are often perceived as being harder to analyze than their unweighted counterparts. Here we point out that weighted networks can in many cases be analyzed using a simple mapping from a weighted network to an unweighted multigraph, allowing us to apply standard techniques for unweighted graphs to weighted ones as well. We give a number of examples of the method, including an algorithm for detecting community structure in weighted networks and a simple proof of the maximum-flow-minimum-cut theorem.}, issue = {5 2}, journaltitle = {Physical Review E - Statistical, Nonlinear, and Soft Matter Physics}, date = {2004}, author = {Newman, M. E J}, file = {/home/dimitri/Nextcloud/Zotero/storage/Q8YDE5VC/Newman - 2004 - Analysis of weighted networks.pdf}, eprinttype = {pmid}, eprint = {15600716} }

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