Centrality In Social Networks: Theoretical And Simulation Approaches

Abstract. Centrality is an important concept in the study of social networks, which in turn are important in studying organisational and team behaviour. For example, “central” individuals influence information flow and decision-making within a group. However, the relationship between mathematical measures of centrality on the one hand, and the real-world phenomenon of centrality on the other, is somewhat unclear. In this paper, we provide two additional perspectives: an analysis of real-world social-network data, and a study of networks produced by a simulation process. Comparing the two perspectives leads to recommendations on when to use different centrality measures. 1. INTRODUCTION Social Network Analysis [1] is an important tool for studying organisational structures. When simulating organisations, Social Network Analysis concepts are of great value in interpreting the results [2]. Within Social Network Analysis, centrality is an important concept [1]. High centrality scores identify actors with the greatest structural importance in networks, and these actors would be expected to have a key role in simulated and real-world behaviour. This applies to networks of many different kinds [1]. tSeveral methods for measuring centrality exist, and this raises the question: which of these methods should be used? The difficulty in answering this question is that centrality is a sociological concept, but there is no well-defined