Estimating the global density of graphs by a sparseness index

Salvatore Menniti, Emanuele Castagna, Tommaso Mazza

Research output: Contribution to journalArticlepeer-review


Computation of sparse matrix is key in a wide range of applications of science and engineering. Matrix is tightly bound to the graph data structure and frequently used as an effective alternative: the complexity of fairly complicated operations on graphs can be measured as the computer time required to execute a number of arithmetic operations on nonzero quantities of a matrix. In this perspective, sparse matrices are computationally advantageous. We present a rigorous refinement of the completeness index, a mathematical function designed to quantify the sparsity of matrices. We prove its mathematical properties as well as its usefulness in the biological realm.

Original languageEnglish
Pages (from-to)346-357
Number of pages12
JournalApplied Mathematics and Computation
Publication statusPublished - 2013


  • Biological networks
  • Network connectivity
  • Sparse matrix

ASJC Scopus subject areas

  • Applied Mathematics
  • Computational Mathematics


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