Clustering coefficient and random networks
Knowing that the clustering coefficient is a property of a specific node of a network that try to represent how well connected the neighborhood of the node is, and can be calculated as following for node i: Ci = 2Li/(ki*(ki-1)) Ci = 0 if ki <= 1 where: Ci = Clustering coefficient Li = Number of links between node i’s neighbors ki = Degree of node i Given the following graph: And the following affirmatives, which alternative attend the following situations respectively: A vertex that will have a fully connected neighborhood after its removal The vertex that has the lowest clustering coefficient greater than 0 In a random network the clustering coefficient can be represented as: Ci=p=<Li>/N, where p is the probability of the existence of an edge between two nodes. Node F, Node C, true Node E, Node B, false Node C, Node D, false Node F, Node B, true None of the above Original idea by: Hitalo Cesar Alves
