Exploration of Centrality Measures and Network Flows using Simulation Studies
Identifying the central people in information flow networks is essential in 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 an inappropriate metric is used for a 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 paths, trails, geodesics and walks. We find that, unlike what is suggested in previous literature, surprisingly Information Centrality is more similar to Eigenvector and Degree centrality than to Closeness centrality.
Chintan Amrit is Associate Professor at the department of Operations Management, at the University of Amsterdam. He has completed his PhD from IEBIS department of the University of Twente in the area of Software Mining, having started it at RSM Erasmus University. He holds a master’s degree in Computer Science from Indian Institute of Science, Bangalore.