Journal article
High quality multi-core multi-level algorithm for community detection
International Journal of Computational Science and Engineering, Vol.15(3-4), pp.311-321
2017
DOI: 10.1504/IJCSE.2017.087399
Abstract
One of the most relevant and widely studied structural properties of networks is their community structure or clustering. Detecting communities is of great importance in various disciplines where systems are often represented as graphs. Different community detection algorithms have been introduced in the past few years, which look at the problem from different perspectives. Most of these algorithms, however, have expensive computational time that makes them impractical to use for large graphs found in the real world. Maintaining a good balance between the computational time and the quality of the communities discovered is a well-known open problem in this area. In this paper, we propose a multi-core multi-level (MCML) community detection algorithm based on the topology of the graph, which contributes towards solving the above problem. MCML algorithm on two benchmark datasets results in detection of accurate communities. We detect high modularity communities by applying MCML on Facebook Forum dataset to find users with similar interests and Amazon product dataset. We also show the scalability of MCML on these large datasets with 16 Xeon Phi cores.
Details
- Title: Subtitle
- High quality multi-core multi-level algorithm for community detection
- Creators
- Suely Oliveira - 1Department of Computer Science, University of Iowa, Iowa, IA-52246, USARahil Sharma - 2Department of Computer Science, University of Iowa, Iowa, IA-52246, USA
- Resource Type
- Journal article
- Publication Details
- International Journal of Computational Science and Engineering, Vol.15(3-4), pp.311-321
- Publisher
- Inderscience Publishers (IEL)
- DOI
- 10.1504/IJCSE.2017.087399
- ISSN
- 1742-7185
- eISSN
- 1742-7193
- Language
- English
- Date published
- 2017
- Academic Unit
- Mathematics; Computer Science
- Record Identifier
- 9984002461202771
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