Real-Time Community Detection in Full Social Networks on a Laptop

DOI

The project analysed the performance of community detection algorithms on the Twitter social network operating on a graph compressed using minhash signatures. The data supplied gives minhash signatures of roughly 16,000 Twitter users who have been classified into 16 categories. It is described in https://arxiv.org/abs/1601.03958 and together with code at https://github.com/melifluos/LSH-community-detection allows the results within to be replicated.

Date Accepted: 2017-11-14

Identifier
DOI https://doi.org/10.17026/dans-2bc-4qgc
Metadata Access https://phys-techsciences.datastations.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.17026/dans-2bc-4qgc
Provenance
Creator B.P. Chamberlain
Publisher DANS Data Station Phys-Tech Sciences
Contributor BP Chamberlain
Publication Year 2017
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact BP Chamberlain (Imperial College London)
Representation
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Version 1.0
Discipline Other