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Question f and g only please complex networksConsider the following model to grow simple networks. At time t = 1 we start with a complete

Question f and g only please complex networksConsider the following model to grow simple networks. At time t=1 we start with a complete network with n0=6 nodes. At each time step t>1 a new node is added to the network. The node arrives together with m=2 new links, which are connected to m=2 different nodes already present in the network. The probability i that a new link is connected to node i is:
i=ki-1Z, with Z=j=1N(t-1)(kj-1)
where ki is the degree of node i, and N(t-1) is the number of nodes in the network at time t-1.
(a) Find an expression for the number of nodes, N(t), and the number of links, L(t), in the network as a function of time t. Find an expression for the value of Z as a function of time t.
(b) What is the average node degree (:k:) at time t? What is the average node degree in the limit t?
(c) Write down the differential equation governing the time evolution of the degree ki of node i for t1 in the mean-field approximation. Solve this equation with the initial condition ki(ti)=m, where ti is the time of arrival of node i.
(d) Derive the degree distribution P(k) of the network for t1 in the mean-field approximation. Does the model produce scale-free networks? If so, what is the value of the degree exponent ?
(e) Write down the master equation of the model, i.e. the equation that describes the evolution of the average number Nk(t) of nodes that at time t have degree k.
(f) Suppose now that you iterate the growing process for a finite number of time steps, until you produce a final network with N=106 nodes (and a minimum degree m=2). Denote as K the natural cutoff in the network. Treating the degree k as a continuous variable, evaluate the natural cutoff K, the normalisation constant in the degree distribution, the average degree (:k:), and (:k2:).
(g) Calculate the probability of finding a node with 1000 links in the network obtained in point (f). Calculate the probability of finding a node with 1000 links in a Erds-Rnyi random graphs with the same number of nodes and links as in the network obtained in point (f).
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