On the Efficiency of Data Representation on the Modeling and Characterization of Complex Networks

TL;DR

This paper compares static matrix and dynamic sparse representations of complex networks, showing that sparse structures often significantly improve execution times in network analysis tasks.

Contribution

It provides a systematic comparison of representation methods for complex networks, highlighting the impact on computational efficiency across different models.

Findings

Sparse representations generally outperform matrix representations in execution time.

The choice of data structure significantly affects the efficiency of network property calculations.

Different network models respond differently to representation choices.

Abstract

Specific choices about how to represent complex networks can have a substantial effect on the execution time required for the respective construction and analysis of those structures. In this work we report a comparison of the effects of representing complex networks statically as matrices or dynamically as spase structures. Three theoretical models of complex networks are considered: two types of Erdos-Renyi as well as the Barabasi-Albert model. We investigated the effect of the different representations with respect to the construction and measurement of several topological properties (i.e. degree, clustering coefficient, shortest path length, and betweenness centrality). We found that different forms of representation generally have a substantial effect on the execution time, with the sparse representation frequently resulting in remarkably superior performance.