THESIS
2019
ix, 73 pages, 15 unnumbered pages : illustrations (some color) ; 30 cm
Abstract
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...[
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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.
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