Measuring the robustness of network community structure using assortativity.

ABSTRACT: The existence of discrete social clusters, or 'communities', is a common feature of social networks in human and nonhuman animals. The level of such community structure in networks is typically measured using an index of modularity, Q. While modularity quantifies the degree to which individuals associate within versus between social communities and provides a useful measure of structure in the social network, it assumes that the network has been well sampled. However, animal social network data is typically subject to sampling errors. In particular, the associations among individuals are often not sampled equally, and animal social network studies are often based on a relatively small set of observations. Here, we extend an existing framework for bootstrapping network metrics to pro

SUBMITTER: Shizuka D 

PROVIDER: S-EPMC4758825 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature