Dynamic Community Finding - UCD Digital Library
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Dynamic Community Finding : Benchmark data & software

Abstract: Real-world social networks from a variety of domains can naturally be modeled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Therefore, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking communities which persist over time in dynamic networks, where each community is characterized by a series of significant evolutionary events. This model is used to motivate a scalable community-tracking strategy for efficiently identifying dynamic communities.

In collection Insight Centre for Data Analytics

# Label Extent MIME type Data
Dynamic Community Finding : Benchmark data 1 zip-compressed file archive
5701476 bytes
Dynamic Community Tracking Tool (Software) : Linux 64-bit binary 1 gzip-compressed file archive
144967 bytes
Dynamic Community Tracking Tool (Software) : Mac OSX 10.6 64-bit binary 1 gzip-compressed file archive
133989 bytes
Date issued
Date issued:
Type of resource
software, multimedia
Physical description
1 zip-compressed file archive
5701476 bytes — Digital origin: born digital (application/zip)
MD5 checksum: 73fe4d9b0f1ca4f3f15d64d105c8da58
Dynamic_benchmark.zip
Downloaded from the Insight Centre web site at http://mlg.ucd.ie/dynamic/index.html, 2014-08-27
Note
3 sets of 4 types of dynamic benchmark graphs, containing embedded disjoint and overlapping communities
Note
Supplementary material for the paper: D. Greene, D. Doyle, and P. Cunningham. (2010), "Tracking the evolution of communities in dynamic social networks". Proc. International Conference on Advances in Social Networks Analysis and Mining (ASONAM’10) (Second Best Paper Award)
Datasets
3 sets of 4 types of dynamic benchmark graphs, containing embedded disjoint and overlapping communities. These datasets were created using the following dynamic network generator. This tool is based on the static network generation tool written by Andrea Lancichinetti & Santo Fortunato.
Genre
Dataset   linked data (dct)
Funding
Funded by a grant from Science Foundation Ireland, Grant No. 08/SRC/I1407 (Clique: Graph & Network Analysis Cluster).

Record source
Prepared by staff of UCD Library, University College Dublin

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