  Identifying the central people in information flow networks is essential to
understanding how people communicate and coordinate as well as who controls the
information flows in the network. However, the appropriate usage of centrality
metrics depends on an understanding of the type of network flow. Networks can
vary in the way node-to-node transmission takes place, or in the way a course
through the network is taken, thereby leading to different types of information
flow processes. When metrics are used for an inappropriate flow process, the
result of the metric can be misleading and often incorrect. In this paper we
create a simulation of the flow of information in a network, and then we
investigate the relation of information centrality as well as other network
centralities, like betweenness, closeness and eigenvector along with the
outcome of simulations with information flowing through walks rather than
paths, trails or geodesics. We find that Information Centrality is more similar
to Eigenvector and Degree centrality than to Closeness centrality as postulated
by previous literature. We also find an interesting pattern emerge from the
inter metric correlations.
