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

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.

Identifier
DOI https://doi.org/10.17026/dans-2bc-4qgc
PID https://nbn-resolving.org/urn:nbn:nl:ui:13-s8-s2yw
Related Identifier https://arxiv.org/abs/1601.03958
Related Identifier https://github.com/melifluos/LSH-community-detection
Metadata Access https://easy.dans.knaw.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:easy.dans.knaw.nl:easy-dataset:76304
Provenance
Creator Chamberlain, B.P.
Publisher Data Archiving and Networked Services (DANS)
Publication Year 2017
Rights info:eu-repo/semantics/openAccess; License: http://creativecommons.org/publicdomain/zero/1.0; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Representation
Language English
Resource Type Dataset
Format TXT; CSV; PDF; MD; PY; PYX; SO
Discipline Other