CIShell Manual : Blondel Community Detection
This page last changed on Jan 12, 2011 by dapolley.
DescriptionSee ALGDOC:Links and ALGDOC:References. ApplicationsThe directionality of the input network does not matter, so both directed and undirected networks yield the same results. If the input network has numeric edge attributes, one can be chosen as edge weight. If no edge weight (attribute) is specified, all edges default to having a weight of 1. The output network will structurally be the same as the input network, but the nodes will be annotated with new attributes labeled "blondel_community_level_x", where x is a community level. The value of each of these attributes is the id of a community. Communities with the same id, but across multiple community levels are separate communities. Also, the lower the community level, the larger the communities in that level are likely to be. Implementation DetailsA single network is expected as the input, and a single network is produced as the output. A modified version of the C++ implementation of this algorithm is compiled and wrapped for integration into CIShell (see Links and References). This version is modified to output the community tree to a file called "communities.tree", instead of printing it to standard output. To integrate this algorithm in CIShell, a custom (Java) converter is used to convert the input network file to a binary edge list file that is proprietary to the compiled algorithm. The compiled algorithm is then executed upon this proprietary binary edge list. The output "communities.tree" file is merged with the input network to produce the output network with annotations. Usage HintsThe output of this algorithm can be visualized well with the Circular Hierarchy visualization. Links
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Document generated by Confluence on May 31, 2011 16:37 |