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This page is a list of network theory topics. Network theorems. Max flow min cut theorem; Menger's theorem; Metcalfe's law; Network properties. Centrality;
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) compone
The first consists of a cohesive core sub-graph in which the nodes are highly interconnected, and the second is made up of a peripheral set of nodes that is loosely connected to the core. In an ideal core–periphery matrix, core nodes are adjacent to other core nodes and to some peripheral nodes while peripheral nodes are not connected with ...
In elementary network theory, a network is represented by a graph = (,) in which is the set of nodes and the links between nodes, typically represented as a tuple of nodes ,. While this basic formalization is useful for analyzing many systems, real world networks often have added complexity in the form of multiple types of relations between ...
The degree of a node in a network (sometimes referred to incorrectly as the connectivity) is the number of connections or edges the node has to other nodes. If a network is directed, meaning that edges point in one direction from one node to another node, then nodes have two different degrees, the in-degree, which is the number of incoming edges, and the out-degree, which is the number of ...
Betweenness centrality finds wide application in network theory; it represents the degree to which nodes stand between each other. For example, in a telecommunications network , a node with higher betweenness centrality would have more control over the network, because more information will pass through that node.
Pages in category "Network theory" The following 98 pages are in this category, out of 98 total. This list may not reflect recent changes. ...
The key advantage of the Configuration Model lies in its ability to decouple the degree sequence from specific edge generation processes. [3] This makes it suitable for analyzing networks with heterogeneous degree distributions, such as scale-free networks, which exhibit heavy-tailed degree distributions.