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Network problems that involve finding an optimal way of doing something are studied as combinatorial optimization.Examples include network flow, shortest path problem, transport problem, transshipment problem, location problem, matching problem, assignment problem, packing problem, routing problem, critical path analysis, and program evaluation and review technique.
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;
Fig. 1: A sketch of a small network displaying community structure, with three groups of nodes with dense internal connections and sparser connections between groups.. In the study of networks, such as computer and information networks, social networks and biological networks, a number of different characteristics have been found to occur commonly, including the small-world property, heavy ...
Identifying core–periphery structures can help circumvent this problem by categorizing hubs as part of the network's core (Rombach et al., 2014, p. 160). Likewise, though all core nodes have high centrality measures, not all nodes with high centrality measures belong to the core.
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.
Random linear network coding [9] (RLNC) is a simple yet powerful encoding scheme, which in broadcast transmission schemes allows close to optimal throughput using a decentralized algorithm. Nodes transmit random linear combinations of the packets they receive, with coefficients chosen randomly, with a uniform distribution from a Galois field.
There are several types of secret sharing schemes. The most basic types are the so-called threshold schemes, where only the cardinality of the set of shares matters. In other words, given a secret S, and n shares, any set of t shares is a set with the smallest cardinality from which the secret can be recovered, in the sense that any set of t − 1 shares is not enough to give S.
Assortativity, or assortative mixing, is a preference for a network's nodes to attach to others that are similar in some way.Though the specific measure of similarity may vary, network theorists often examine assortativity in terms of a node's degree. [1]