Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from different domains may vary quite significantly. As there is an interplay between network architecture and dynamics, structure plays an important role in communication and spreading of information in a network. Here we investigate the underlying undirected topology of different biological networks which support faster spreading of information and are better in communication. We analyse the good expansion property by using the spectral gap and communicability between nodes. Different epidemic models are also used to study the transmission of information in terms of spreading of disease through individuals (nodes)in those networks. Moreover, we explore the structural conformation and properties which may be responsible for better communication. Among all biological networks studied here, the undirected structure of neuronal networks not only possesses the small-world property but the same is also expressed remarkably to a higher degree compared to any randomly generated network which possesses the same degree sequence. A relatively high percentage of nodes, in neuronal networks, form a higher core in their structure. Our study shows that the underlying undirected topology in neuronal networks, in a significant way, is qualitatively different from the same in other biologicalnetworks and that they may have evolved in such a way that they inherit a (undirected) structure which is excellent and robust in communication.
Volume 94, 2020
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