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.
  1. Node F, Node C, true
  2. Node E, Node B, false
  3. Node C, Node D, false
  4. Node F, Node B, true
  5. None of the above

 Original idea by: Hitalo Cesar Alves

 


Comentários

  1. Joao Meidanis2 de abril de 2023 às 08:29

    Interesting question, but ir brings nothing new, and mixes statements on nodes with general properties of random networks. Confusing.

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