## Abstract

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 language | English |
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Pages (from-to) | 346-357 |

Number of pages | 12 |

Journal | Applied Mathematics and Computation |

Volume | 224 |

DOIs | |

Publication status | Published - 2013 |

## Keywords

- Biological networks
- Network connectivity
- Sparse matrix

## ASJC Scopus subject areas

- Applied Mathematics
- Computational Mathematics