Analysis of weighted networks

by: MEJ Newman

(20 July 2004)

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arXiv (abstract), arXiv (PDF)

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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 new and simple proof of the max-flow/min-cut theorem.

@misc{citeulike:281, 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 new and simple proof of the max-flow/min-cut theorem.}, author = {Newman, M. E. J. }, citeulike-article-id = {281}, eprint = {cond-mat/0407503}, keywords = {analysis, graphs, network}, month = {July}, posted-at = {2008-05-05 17:35:44}, priority = {0}, title = {Analysis of weighted networks}, url = {http://arxiv.org/abs/cond-mat/0407503}, year = {2004} }

RIS record

TY - ELEC ID - citeulike:281 L3 - citeulike-article-id:281 TI - Analysis of weighted networks N2 - 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 new and simple proof of the max-flow/min-cut theorem. KW - analysis KW - graphs KW - network AU - Newman, MEJ PY - 2004/07/20/ UR - http://arxiv.org/abs/cond-mat/0407503 ER -

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