# Random Alpha PageRank

See Gleich and Constantine, Random Alpha PageRank, for details about this method. PageRank is a method to evaluate the importance of nodes in a directed graph. On the graph, we follow a random walk where at each step, with probability a, we follow a randomly chosen link from the current node with probability (1-a), we jump to a randomly chosen node. (Both random choices are uniform over all the possibilities.) The stationary distribution of this Markov process is the PageRank vector. We can compute the stationary distribution by solving a linear system,

where P is the transition matrix for a random walk on the graph. In Random Alpha PageRank, we place a with a random variable, and examine the expected value of x. This computation is solved by a parameterized matrix problem.

For more information, see

Constantine, P. G. and Gleich, D. F. Random Alpha PageRank, Internet Mathematics, To appear.

Usually, we pick a Beta random variable for a -- this corresponds to approximating x(a) with a Jacobi parameter.

## Contents

## Load a webgraph and setup the linear system

In this step, we construct the parameterzied linear system that correponds to PageRank

load('wb-cs.stanford.mat'); % we load a small web-graph matrix P = Pcc; % only use the largest strong component -- this fixes a few technical details A = @(a) (speye(size(P)) - a*P'); b = @(a) (1-a)./size(P,1)*ones(size(P,1),1); iAb = @(a) A(a)\b(a); % this function solves for a PageRank vector

## Compute a PageRank vector

Now, let's compute a single PageRank vector and show the vector of ''Ranks'' in sorted order. This problem corresponds to a surfer than will follow a link 85% of the time, or make random jumps 15% of the time.

x85 = iAb(0.85); semilogy(sort(x85));

This plot shows that most of the values are small 1e-4 to 1e-3, but that a few are large.

## Setup the parameter

In random alpha PageRank, we consider a population of surfers where the link following probabilities, which are the values of a, are generated from a distribution. The parameter s below is a Jacobi parameter to model the randomness in the value of a. The two shape parameters of the Jacobi parameter give it a slight shift towards larger values of a.

s = [jacobi_parameter(0,1,2,3)];

There's no good PMPack function to plot the density of such a function. The following code draws a beta pdf, which is the density for a Jacobi parameter.

beta_a = 4; % the code to evaluate the pdf uses beta_a = b+1 = 3+1 beta_b = 3; % and beta_b = a+1 = 2+1 xx = 0:0.01:1; logkerna = (beta_a-1).*log(xx); logkernb = (beta_b-1).*log(1-xx); plot(xx,exp(logkerna+logkernb - betaln(beta_a,beta_b)));

This figure shows how often surfers will ``draw'' different values of a, the link following probabilitiy.

## Compute the approximation

At this point, we can compute approximations of the expected value of the Random Alpha PageRank function

```
[X,errz] = pseudospectral(iAb,s,'adapt');
```

## Get the expected solution

The first vector in the coefficients matrix is just the expected solution. It is the expected value of the vector iAb over a drawn from a Beta density.

ex = X.coefficients(:,1);

Let's compare that against the deterministic solution

loglog(x85,ex,'.'); xlabel('Deterministic PageRank'); ylabel('Expected PageRank with Jacobi(2,3) parameter');

The vector has changed quite a bit! There's a pleasing curve in the plot above showing that the values with a Jacobi(2,3) are more compressed than their deterministic counterparts. For wild behavior, try a Jacobi(0.2,0.6,0.5,0.5) parameter

## Plot the first component

Now we show how a single component of the vector changes as a function of a.

first_component = @(x) x(1); fplot(@(a) first_component(evaluate_expansion(X,a)), [0,1]); ylabel('x_1(a)'); xlabel('a');