Complex networks are widely applied in the modeling and analysis of complex systems, and inferring correlation between different parts of the system is a fundamental problem in network science. In this work, we focus on one measure of the correlation between two nodes in a network, named communicability, and compare it with other measurements of correlations or “distances”. Since the communicability can also be interpreted as the efficiency of information transfer, by taking the average of communicability between all pairs of nodes, we may use the “average communicability” to evaluate the ability of information exchange within the whole network. Average communicability of different network models are studied and the problem of increasing the average communicability within certa...[ Read more ]

Complex networks are widely applied in the modeling and analysis of complex systems, and inferring correlation between different parts of the system is a fundamental problem in network science. In this work, we focus on one measure of the correlation between two nodes in a network, named communicability, and compare it with other measurements of correlations or “distances”. Since the communicability can also be interpreted as the efficiency of information transfer, by taking the average of communicability between all pairs of nodes, we may use the “average communicability” to evaluate the ability of information exchange within the whole network. Average communicability of different network models are studied and the problem of increasing the average communicability within certain steps of modification of the network topology is discussed.