Identifying relationship patterns inside communities
Community detection is an important problem for Computer and other sciences. Following Agarwal and Kempe one of the most important reasons to make clustering over a network is to identify the function/role of each element in a community. If the communities have hundreds or thousands of elements, it...
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| Autores principales: | , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2014
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/41847 http://43jaiio.sadio.org.ar/proceedings/IJCAI/17-18.pdf |
| Aporte de: |
| Sumario: | Community detection is an important problem for Computer and other sciences. Following Agarwal and Kempe one of the most important reasons to make clustering over a network is to identify the function/role of each element in a community.
If the communities have hundreds or thousands of elements, it is important to understand the functions of internal elements, but that will require an automatic process. In this context, we propose to develop a model, capable to identify elements with common features in different communities, based on the connection between elements and communities, agreeing with Newman and Girvan model features.
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