Improved community detection inweighted bipartite networks

dc.contributor.author	Beckett, Stephen J.	en_US
dc.date.accessioned	2016-07-30T01:39:24Z
dc.date.available	2016-07-30T01:39:24Z
dc.date.issued	2016	en_US
dc.identifier.other	HPU4160522	en_US
dc.identifier.uri	https://lib.hpu.edu.vn/handle/123456789/22421	en_US
dc.description.abstract	Real world complex networks are composed of non random quantitative interactions. Identifying communities of nodes that tend to interact more with each other than the network as a whole is a key research focus across multiple disciplines, yet many community detection algorithms only use information about the presence or absence of interactions between nodes. Weighted modularity is a potential method for evaluating the quality of community partitions in quantitative networks.	en_US
dc.format.extent	18 p.	en_US
dc.format.mimetype	application/pdf	en_US
dc.language.iso	en	en_US
dc.subject	Biology	en_US
dc.subject	Graph theory	en_US
dc.subject	Computational biology	en_US
dc.subject	Ecology	en_US
dc.subject	Modular structure	en_US
dc.subject	Network ecology	en_US
dc.subject	Bipartite networks	en_US
dc.subject	Modules	en_US
dc.title	Improved community detection inweighted bipartite networks	en_US
dc.type	Article	en_US
dc.size	736KB	en_US
dc.department	Education	en_US