Estimating the divisibility of complex biological networks by sparseness indices

Tommaso Mazza, Alessandro Romanel, Ferenc Jordán

Research output: Contribution to journalArticlepeer-review


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 aprioriinsight 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.

Original languageEnglish
Article numberbbp060
Pages (from-to)364-374
Number of pages11
JournalBriefings in Bioinformatics
Issue number3
Publication statusPublished - Jan 11 2010


  • Biologicalnetworks
  • Graph sparseness
  • Parallel computing
  • Partitioning

ASJC Scopus subject areas

  • Molecular Biology
  • Information Systems


Dive into the research topics of 'Estimating the divisibility of complex biological networks by sparseness indices'. Together they form a unique fingerprint.

Cite this