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Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges).
v. t. e. In mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the vertices or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components.
In network science, a hub is a node with a number of links that greatly exceeds the average. Emergence of hubs is a consequence of a scale-free property of networks. [1]: 27 While hubs cannot be observed in a random network, they are expected to emerge in scale-free networks. The uprise of hubs in scale-free networks is associated with power ...
Similarity in network analysis occurs when two nodes (or other more elaborate structures) fall in the same equivalence class. There are three fundamental approaches to constructing measures of network similarity: structural equivalence, automorphic equivalence, and regular equivalence. [1] There is a hierarchy of the three equivalence concepts ...
In network science, the efficiency of a network is a measure of how efficiently it exchanges information [1] and it is also called communication efficiency. The underlying idea (and main assumption) is that the more distant two nodes are in the network, the less efficient their communication will be. The concept of efficiency can be applied to ...
In network science, reciprocity is a measure of the likelihood of vertices in a directed network to be mutually linked. [1] Like the clustering coefficient, scale-free degree distribution, or community structure, reciprocity is a quantitative measure used to study complex networks.
In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems. The study of complex networks is a young and active area of scientific research [1][2] (since 2000 ...
v. t. e. This is a list of notable individuals who research complex networks, including social networks, biological networks, and semantic networks, among others. Individuals are categorized based on their background and training, or their area of focus.