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How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection
By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2(n)), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the num...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4156142/ https://www.ncbi.nlm.nih.gov/pubmed/25294962 http://dx.doi.org/10.1098/rspa.2014.0224 |
Sumario: | By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2(n)), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size. |
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