Brief. Bioinformatics - Estimating the divisibility of complex biological networks by sparseness indices.

Tópicos

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Resumo

In order to understand the complex relationships among the components of biological systems, network models have been used for a long time. Although they have been extensively used for visualization, data storage, structural analysis and simulation, some computational processes are still very inefficient when applied on complex networks. In particular, any parallel simulation technique requires a network previously divided into a number of clusters in numbers equal to that of the available processors. At the same time, let maximally disconnected clusters be chosen in order to minimize extra-communication overhead and to optimize the overall computational efficiency. Obtaining such a disconnection becomes a computationally hard problem when disconnection conditions are complex in themselves, like in the case of parallel simulation. Before applying any clustering method, topological indices might contribute to give an a priori insight about the divisibility of a network. Here we present a class of them, the sparseness indices. As particular topological indices provide either local or global quantification of network structure, they can help in identifying locally dense, but globally sparsely connected subgraphs.

Resumo Limpo

order understand complex relationship among compon biolog system network model use long time although extens use visual data storag structur analysi simul comput process still ineffici appli complex network particular parallel simul techniqu requir network previous divid number cluster number equal avail processor time let maxim disconnect cluster chosen order minim extracommun overhead optim overal comput effici obtain disconnect becom comput hard problem disconnect condit complex like case parallel simul appli cluster method topolog indic might contribut give priori insight divis network present class spars indic particular topolog indic provid either local global quantif network structur can help identifi local dens global spars connect subgraph

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