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Problem 2.1
Let the directed graph G be a ring: node i is connected to i + 1 if i < m and m is connected to 1. Compute
both eigenvalue centrality and Katz centrality (with = 1). Comment on your result. Do the same for a k-regular
undirected network (i.e., an undirected network in which every vertex has degree k). You may find the steps
outlined in Newman Problem 7.1 helpful. Comment on your result.
Solution:
(a) Eigenvector Centrality: For an adjacency matrix A, the eigenvector with the highest eigenvalue
represents the eigenvector centrality of each node in A. G is an orthogo-nal matrix with the highest
eigenvalue 1 and corresponding eigenvector
Here we prove that G can not have an

Here we show that 1 is an eigenvalue of A because its an orthogonal matrix with unit vectors
This means that all eigenvalues of G 1. But since G is orthogonal, all of its eigenvalues lie on the complex
plane wit odulus 1 which means
Naturally, all nodes have equal centrality (both eigenvector and katz) due to symmetry.

Setting x1 = ... = xn = x and c = 1 and as the attenuating factor, we can write
Thus, the centralities increase as k increases. All nodes have equal centrality (both eigenvector and katz)
due to symmetry.
Problem 2.2
[Problem 7.2 from Newman] Suppose a directed network takes the form of a tree with all edges pointing
inward towards a central vertex: (see figure in Newman). What is the PageRank centrality of the central
vertex in terms of the single parameter ↵ appearing in the definition of PageRank and the geodesic
distances di from each vertex i to the central vertex?

Solution:
We want to computer the Pagerank centrality of xc of the central vertex and we assume that C = 1. Notice
that 3 nodes have distance 3 from c, 4 nodes have distance 2 from c and 2 nodes have distance 1 from c.
Generalizing from the pattern any node that is distance d from c will have its centrality scaled with ↵ d
number of times, towards the computation of xc.
Therefore,

Problem 2.3
(a) Give an example of such a graph with 5 nodes.
(b) Compute the eigenvalue centrality. Explain your answer.
(c) Compute the Katz centrality with = 1. Explain your answer.
Solution:
(a) Any directed acyclic graph would do. Here is an example given below

(b) Since A is nilpotent, the characteristic polynomial is t n = 0 and all the eigenvalues i are zero. So it does
not make sense to look at the eigenvector centrality, defined as the eigenvector associated with the largest
eigenvalue, max.
(c) The katz centrality is computed iteratively using
. Picking ↵ = 0.3, for instance, and = 1 we get (after 4 iterations)

ckatz = [1 1.3 1.39 1.417 1.4251]
which provides a sensible ranking if we take incoming edges to indicate importance.
Problem 2.4
As flu season is upon us, we wish to have a Markov chain that models the spread of a flu virus. Assume a
population of n individuals. At the beginning of each day, each individual is either infected or susceptible
(capable of contracting the flu). Suppose that each pair (i, j), i 6= j, independently comes into contact with one
another during the daytime with probability p. Whenever an infected individual comes into contact with a
susceptible individual, he/she infects him/her. In addition, assume that overnight, any individual who has been
infected for at least 24 hours will recover with probability 0 <q< 1 and return to being susceptible, independently
of everything else (i.e., assume that a newly infected individual will spend at least one restless night battling the
flu)
(a) Suppose that there are m infected individuals at daybreak. What is the distribution of the number of
new infections by day end?
(b) Draw a Markov chain with as few states as possible to model the spread of the flu for n = 2. In
epidemiology, this is called an SIS (SusceptibleInfected-Susceptible) model.
(c) Identify all recurrent states.
Due to the nature of the flu virus, individuals almost always develop immunity after contracting the virus.
Consequently, we improve our model and assume that individuals become infected at most one time. Thus,
we consider individuals as either infected, susceptible, or recovered.
(d) Draw a Markov chain to model the spread of the flu for n = 2. In epidemiology, this is called an SIR
(Susceptible-Infected-Recovered) model.
(e) Identify all recurrent states.
Solution:

(a) If m out of n individuals are infected, then there must be n m susceptible individuals. Each one of these
individuals will be independently infected over the course of the day with probability p = 1 - (1 - p )m.
Thus the number of new infections, I, will be a binomial random variable with parameters n m and p. That is,
(b) Let the state of the SIS model be the number of infected individuals. For n = 2, the corresponding Markov
chain is illustrated below.

(c) The only recurrent state is the one with 0 infected individuals
(d) Let the state of the SIR model be (S,I), where S is the number of susceptible individuals and I is
the number of infected individuals. For n = 2, the corresponding Markov chain is illustrated below

If one did not wish to keep track of the breakdown of susceptible and recovered individuals when no
one was infected, the three states free of infections could be consolidated into a single state as
illustrated below
(e) Any state where the number of infected individuals equals 0 is a recurrent state. For n = 2,there are
either one or three recurrent states, depending on the Markov chain drawn in part(d).

Problem 2.5
There are n fish in a lake, some of which are green and the rest blue. Each day, Helen catches 1 fish. She is
equally likely to catch any one of the n fish in the lake. She throws back all the fish, but paints each green fish
blue before throwing it back in. Let Gi denote the event that there are i green fish left in the lake.
(a) Show how to model this fishing exercise as a Markov chain, where Gi are the states. Explain why
your model satisfies the Markov property.
(b) Find the transition probabilities pij
(c) List the transient and recurrent states
Solution:
(a) The number of remaining green fish at time n completely determines all the relevant information of the
systems entire history (relevant to predicting the future state). Therefore it is immediate that the number of
green fish is the state of the system and the process has the Markov property:
(b) For j > i clearly pij = 0, since a blue fish will never be painted green. For 0 i, j k, we have the following

(c) The state 0 is an absorbing state since there is a positive probability that the system will enter it, and
once it does, it will remain there forever. Therefore the state with 0 green fish is the only recurrent state,
and all other states are then transient.

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