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In the context of graph theory for social networks, the notion of centrality is used to assess the relative importance of nodes in a given network topology.
Simulations are used to examine the relationship between type of flow and the differential importance of nodes with respect to key measurements such as speed of reception of traffic and frequency of receiving traffic.
This finding is balanced by the large variations of the contact patterns, which strongly alter the importance of nodes from one period to the next and therefore significantly limit the efficiency of any strategy based on an importance ranking of nodes.
Borgatti [5] defined two types of problems to assess the importance of nodes.
Graph centrality algorithms are designed to measure the importance of nodes within a graph.
In [43], the method of node contraction is introduced to assess the importance of nodes.
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Therefore, the node influence can be worked out so as to evaluate the importance of node in wireless sensor network.
Thus, the sum of two scores R n is defined as an index to evaluate the importance of node n.
We also think it highlights the importance of node topologies on the performance of individual protocols, and hence the need for realistic topology construction in network simulators.
When applying MBCC-HITS algorithm to node importance evaluation of power grid, authority score y n and hub score z n evaluate the importance of node n from perspectives of inflow and outflow power, respectively.
As discussed in the introduction, a variety of methods have been suggested to minimize the importance of node degree.
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