468 research outputs found
Delocalization transition for the Google matrix
We study the localization properties of eigenvectors of the Google matrix,
generated both from the World Wide Web and from the Albert-Barabasi model of
networks. We establish the emergence of a delocalization phase for the PageRank
vector when network parameters are changed. In the phase of localized PageRank,
a delocalization takes place in the complex plane of eigenvalues of the matrix,
leading to delocalized relaxation modes. We argue that the efficiency of
information retrieval by Google-type search is strongly affected in the phase
of delocalized PageRank.Comment: 4 pages, 5 figures. Research done at
http://www.quantware.ups-tlse.fr
Google matrix analysis of DNA sequences
For DNA sequences of various species we construct the Google matrix G of
Markov transitions between nearby words composed of several letters. The
statistical distribution of matrix elements of this matrix is shown to be
described by a power law with the exponent being close to those of outgoing
links in such scale-free networks as the World Wide Web (WWW). At the same time
the sum of ingoing matrix elements is characterized by the exponent being
significantly larger than those typical for WWW networks. This results in a
slow algebraic decay of the PageRank probability determined by the distribution
of ingoing elements. The spectrum of G is characterized by a large gap leading
to a rapid relaxation process on the DNA sequence networks. We introduce the
PageRank proximity correlator between different species which determines their
statistical similarity from the view point of Markov chains. The properties of
other eigenstates of the Google matrix are also discussed. Our results
establish scale-free features of DNA sequence networks showing their
similarities and distinctions with the WWW and linguistic networks.Comment: latex, 11 fig
Anderson transition for Google matrix eigenstates
We introduce a number of random matrix models describing the Google matrix G
of directed networks. The properties of their spectra and eigenstates are
analyzed by numerical matrix diagonalization. We show that for certain models
it is possible to have an algebraic decay of PageRank vector with the exponent
similar to real directed networks. At the same time the spectrum has no
spectral gap and a broad distribution of eigenvalues in the complex plain. The
eigenstates of G are characterized by the Anderson transition from localized to
delocalized states and a mobility edge curve in the complex plane of
eigenvalues.Comment: 9 pages, 12 figs, revte
Google matrix of business process management
Development of efficient business process models and determination of their
characteristic properties are subject of intense interdisciplinary research.
Here, we consider a business process model as a directed graph. Its nodes
correspond to the units identified by the modeler and the link direction
indicates the causal dependencies between units. It is of primary interest to
obtain the stationary flow on such a directed graph, which corresponds to the
steady-state of a firm during the business process. Following the ideas
developed recently for the World Wide Web, we construct the Google matrix for
our business process model and analyze its spectral properties. The importance
of nodes is characterized by Page-Rank and recently proposed CheiRank and
2DRank, respectively. The results show that this two-dimensional ranking gives
a significant information about the influence and communication properties of
business model units. We argue that the Google matrix method, described here,
provides a new efficient tool helping companies to make their decisions on how
to evolve in the exceedingly dynamic global market.Comment: submitted to European Journal of Physics
Google matrix analysis of directed networks
In past ten years, modern societies developed enormous communication and
social networks. Their classification and information retrieval processing
become a formidable task for the society. Due to the rapid growth of World Wide
Web, social and communication networks, new mathematical methods have been
invented to characterize the properties of these networks on a more detailed
and precise level. Various search engines are essentially using such methods.
It is highly important to develop new tools to classify and rank enormous
amount of network information in a way adapted to internal network structures
and characteristics. This review describes the Google matrix analysis of
directed complex networks demonstrating its efficiency on various examples
including World Wide Web, Wikipedia, software architecture, world trade, social
and citation networks, brain neural networks, DNA sequences and Ulam networks.
The analytical and numerical matrix methods used in this analysis originate
from the fields of Markov chains, quantum chaos and Random Matrix theory.Comment: 56 pages, 58 figures. Missed link added in network example of Fig3
Google matrix of the world trade network
Using the United Nations Commodity Trade Statistics Database
[http://comtrade.un.org/db/] we construct the Google matrix of the world trade
network and analyze its properties for various trade commodities for all
countries and all available years from 1962 to 2009. The trade flows on this
network are classified with the help of PageRank and CheiRank algorithms
developed for the World Wide Web and other large scale directed networks. For
the world trade this ranking treats all countries on equal democratic grounds
independent of country richness. Still this method puts at the top a group of
industrially developed countries for trade in {\it all commodities}. Our study
establishes the existence of two solid state like domains of rich and poor
countries which remain stable in time, while the majority of countries are
shown to be in a gas like phase with strong rank fluctuations. A simple random
matrix model provides a good description of statistical distribution of
countries in two-dimensional rank plane. The comparison with usual ranking by
export and import highlights new features and possibilities of our approach.Comment: 14 pages, 13 figures. More detailed data and high definition figures
are available on the website:
http://www.quantware.ups-tlse.fr/QWLIB/tradecheirank/index.htm
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