MC908/MO412 - Hitalo

Which of the following statements is correct about assortative, disassortative and neutral networks? In a perfectly assortative network, it is expected that nodes with degree k connect to nodes of degree from k-1 to k+1. In neutral networks, hubs tend to not connect with other hubs and tend to connect with nodes with low degrees. Assortative networks are networks where hubs tend to connect with other hubs. Disassortative networks are networks where nodes with low degrees tend to connect with other nodes with low degrees and avoid hubs. None of the above

Supposing that we have a Barabasi-Albert model network and a random model network, both with a large and equal number of nodes N. Which statements are correct? The clustering coefficient of the Barabasi-Albert model network is expected to be greater than the random network's one. With the growth of N, the clustering coefficient of the Barabasi-Albert model network grows slower than the random network model's one by a factor of the order of ln(N)^2 The diameter and the clustering coefficient of the Barabasi-Albert model network does not depends on m(the number of connections each new node has when joins the network) in asymptotic situations With the growth of N, the diameter of the Barabasi-Albert model network grows slower than the random network model's one by a factor of the order of ln(ln(N)) 2, 3 and 4 1, 2, 3 and 4 1, 3 and 4 2 and 3 None of the above  Original idea by: Hitalo Cesar Alves  

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  

Given the following projections of a bipartite network : Which of the options below provides a possible Network that generated those projections?  1 2 3 4 None of the above