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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Peel, L. |
| Copyright Year | 2010 |
| Description | Author affiliation: Adv. Technol. Centre, BAE Syst., Bristol, UK (Peel, L.) |
| Abstract | This paper considers the problem of algorithm selection for community detection. The aim of community detection is to identify sets of nodes in a network which are more interconnected relative to their connectivity to the rest of the network. A large number of algorithms have been developed to tackle this problem, but as with any machine learning task there is no “one-size-fits-all” and each algorithm excels in a specific part of the problem space. This paper examines the performance of algorithms developed for weighted networks against those using unweighted networks for different parts of the problem space (parameterised by the intra/inter community links). It is then demonstrated how the choice of algorithm (weighted/unweighted) can be made based only on the observed network. |
| Sponsorship | ISIF |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 605164 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9780982443811 |
| DOI | 10.1109/ICIF.2010.5712065 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-07-26 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Communities Classification algorithms Prediction algorithms Algorithm design and analysis Mutual information Machine learning algorithms Detection algorithms interaction networks Community detection algorithm selection |
| Content Type | Text |
| Resource Type | Article |
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