A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm. The matrix represents a graph with edges representing links between pages. The PageRank of each page can then be generated iteratively from the Google matrix using the power method. However, in order for the power method to converge, the matrix must be stochastic, irreducible and aperiodic.
In order to generate the Google matrix G, we must first generate an adjacency matrix A which represents the relations between pages or nodes.
Assuming there are N pages, we can fill out A by doing the following:
Then the final Google matrix G can be expressed via S as:
By the construction the sum of all non-negative elements inside each matrix column is equal to unity. The numerical coefficient is known as a damping factor.
Usually S is a sparse matrix and for modern directed networks it has only about ten nonzero elements in a line or column, thus only about 10N multiplications are needed to multiply a vector by matrix G.
An example of the matrix construction via Eq.(1) within a simple network is given in the article CheiRank.
For the actual matrix, Google uses a damping factor around 0.85. The term gives a surfer probability to jump randomly on any page. The matrix belongs to the class of Perron-Frobenius operators of Markov chains. The examples of Google matrix structure are shown in Fig.1 for Wikipedia articles hyperlink network in 2009 at small scale and in Fig.2 for University of Cambridge network in 2006 at large scale.
For there is only one maximal eigenvalue with the corresponding right eigenvector which has non-negative elements which can be viewed as stationary probability distribution. These probabilities ordered by their decreasing values give the PageRank vector with the PageRank used by Google search to rank webpages. Usually one has for the World Wide Web that with . The number of nodes with a given PageRank value scales as with the exponent . The left eigenvector at has constant matrix elements. With all eigenvalues move as except the maximal eigenvalue , which remains unchanged. The PageRank vector varies with but other eigenvectors with remain unchanged due to their orthogonality to the constant left vector at . The gap between and other eigenvalue being gives a rapid convergence of a random initial vector to the PageRank approximately after 50 multiplications on matrix.
At the matrix has generally many degenerate eigenvalues (see e.g. ). Examples of the eigenvalue spectrum of the Google matrix of various directed networks is shown in Fig.3 from  and Fig.4 from.
The Google matrix can be also constructed for the Ulam networks generated by the Ulam method  for dynamical maps. The spectral properties of such matrices are discussed in [9,10,11,12,13,15]. In a number of cases the spectrum is described by the fractal Weyl law [10,12].
The Google matrix can be constructed also for other directed networks, e.g. for the procedure call network of the Linux Kernel software introduced in . In this case the spectrum of is described by the fractal Weyl law with the fractal dimension (see Fig.5 from ). Numerical analysis shows that the eigenstates of matrix are localized (see Fig.6 from ). Arnoldi iteration method allows to compute many eigenvalues and eigenvectors for matrices of rather large size .
Other examples of matrix include the Google matrix of brain  and business process management , see also. Applications of Google matrix analysis to DNA sequences is described in . Such a Google matrix approach allows also to analyze entanglement of cultures via ranking of multilingual Wikipedia articles abouts persons