An Analytical Comparison of Social Network Measures

Network science spans many different fields of study, ranging from psychology to biology to the social sciences. A number of descriptive network measures have been identified for use within these fields; however, little research examines the relationships of these measures for possible statistical dependence. The research presented in this paper uses Spearman's rank correlation coefficient to examine the statistical dependence between pairs of 24 widely accepted social network measures. Confidence intervals are compared to determine whether computation times between measures in the same correlation group are significantly different. We use a three-factor, four-level, full-factorial experimental design to construct a test set of 64 unique network topologies. The three factors of interest are the network structural properties of size, cluster ability, and the scale-free parameter. A set of 320 networks are generated from a power law degree distribution using a random graph generation algorithm. Results indicate that there exists high correlation among 14 of the 24 tested network measures, many of which also exhibit statistically significant differences with respect to computation time. These findings are of interest to analysts seeking to identify measures that provide similar ranked outcomes and where computational efficiency is an important consideration.