Networks are widely used to represent the interaction pattern in complex systems. Structures of real networks from different domain may vary quite significantly , and so the dynamicson the network. Which also plays an important role in communication and information spreading on networks . Here we have explored underlying structures of different biological networks which support faster spreading  and are better in communication between nodes in them. In this regard, we have analyzed the good expansion property , from large spectral gap , and communicability between nodes in those networks . Different epidemic models are also used to study the same. Moreover we have explored the structural conformation and properties which might be cause for better communication.  Our study shows that the underlying topology in neural networks is significantly qualitatively different than the same from other biological networks and  they may have evolved  such a way that they inherit a  structure which is excellent and robust in communication.
Networks are widely used to represent interaction pattern among the components in complex systems. Structures of real networks from differ- ent domains may vary quite significantly . Since there is an interplay be- tween network architecture and dynamics, structure plays an important role in communication and information spreading on 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 analyze 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 disease spreading through individuals (nodes) in those networks. More- over, 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 pos- sesses the small-world property but the same is expressed remarkably to a higher degree than 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 is significantly qualitatively different than the same from other biological networks and that they may have evolved in such a way that they inherit a (undirected) structure which is excellent and robust in communication.
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the interaction pattern
interaction pattern among the components
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different domain
differ- ent domains
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, and so the dynamicson the network. Which also
. Since there is an interplay be- tween network architecture and dynamics, structure
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networks
a network
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have explored underlying structures
investigate the underlying undirected topology
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of information
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between nodes in them. In this regard, we have analyzed
. We analyze
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, from large spectral gap ,
by using the spectral gap
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in those networks
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same. Moreover we have explored
transmission of information in terms of disease spreading through individuals (nodes) in those networks. More- over, we explore
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might be cause
may be responsible
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Among all biological networks studied here, the undirected structure of neuronal networks not only pos- sesses the small-world property but the same is expressed remarkably to a higher degree than 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.
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topology in neural
undirected topology in neuronal
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that
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in
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(undirected)