194 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
Reducing the Effects of Unequal Number of Games on Rankings
Ranking is an important mathematical process in a variety of contexts such as information retrieval, sports and business. Sports ranking methods can be applied both in and beyond the context of athletics. In both settings, once the concept of a game has been defined, teams (or individuals) accumulate wins, losses, and ties, which are then factored into the ranking computation. Many settings involve an unequal number of games between competitors. This paper demonstrates how to adapt two sports rankings methods, the Colley and Massey ranking methods, to settings where an unequal number of games are played between the teams. In such settings, the standard derivations of the methods can produce nonsensical rankings. This paper introduces the idea of including a super-user into the rankings and considers the effect of this fictitious player on the ratings. We apply such techniques to rank batters and pitchers in Major League baseball, professional tennis players, and participants in a free online social game. The ideas introduced in this paper can further the scope that such methods are applied and the depth of insight they offer
Influence, originality and similarity in directed acyclic graphs
We introduce a framework for network analysis based on random walks on
directed acyclic graphs where the probability of passing through a given node
is the key ingredient. We illustrate its use in evaluating the mutual influence
of nodes and discovering seminal papers in a citation network. We further
introduce a new similarity metric and test it in a simple personalized
recommendation process. This metric's performance is comparable to that of
classical similarity metrics, thus further supporting the validity of our
framework.Comment: 6 pages, 4 figure
Thermodynamic formalism for dissipative quantum walks
We consider the dynamical properties of dissipative continuous-time quantum
walks on directed graphs. Using a large-deviation approach we construct a
thermodynamic formalism allowing us to define a dynamical order parameter, and
to identify transitions between dynamical regimes. For a particular class of
dissipative quantum walks we propose a quantum generalization of the the
classical PageRank vector, used to rank the importance of nodes in a directed
graph. We also provide an example where one can characterize the dynamical
transition from an effective classical random walk to a dissipative quantum
walk as a thermodynamic crossover between distinct dynamical regimes.Comment: 8 page
Ranking and clustering of nodes in networks with smart teleportation
Random teleportation is a necessary evil for ranking and clustering directed
networks based on random walks. Teleportation enables ergodic solutions, but
the solutions must necessarily depend on the exact implementation and
parametrization of the teleportation. For example, in the commonly used
PageRank algorithm, the teleportation rate must trade off a heavily biased
solution with a uniform solution. Here we show that teleportation to links
rather than nodes enables a much smoother trade-off and effectively more robust
results. We also show that, by not recording the teleportation steps of the
random walker, we can further reduce the effect of teleportation with dramatic
effects on clustering.Comment: 10 pages, 7 figure
Dynamics-based centrality for general directed networks
Determining the relative importance of nodes in directed networks is
important in, for example, ranking websites, publications, and sports teams,
and for understanding signal flows in systems biology. A prevailing centrality
measure in this respect is the PageRank. In this work, we focus on another
class of centrality derived from the Laplacian of the network. We extend the
Laplacian-based centrality, which has mainly been applied to strongly connected
networks, to the case of general directed networks such that we can
quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used
in the PageRank to introduce global connectivity between all the pairs of nodes
with a certain strength. Numerical simulations are carried out on some
networks. We also offer interpretations of the Laplacian-based centrality for
general directed networks in terms of various dynamical and structural
properties of networks. Importantly, the Laplacian-based centrality defined as
the stationary density of the continuous-time random walk with random jumps is
shown to be equivalent to the absorption probability of the random walk with
sinks at each node but without random jumps. Similarly, the proposed centrality
represents the importance of nodes in dynamics on the original network supplied
with sinks but not with random jumps.Comment: 7 figure
Adiabatic quantum algorithm for search engine ranking
We propose an adiabatic quantum algorithm for generating a quantum pure state
encoding of the PageRank vector, the most widely used tool in ranking the
relative importance of internet pages. We present extensive numerical
simulations which provide evidence that this algorithm can prepare the quantum
PageRank state in a time which, on average, scales polylogarithmically in the
number of webpages. We argue that the main topological feature of the
underlying web graph allowing for such a scaling is the out-degree
distribution. The top ranked entries of the quantum PageRank state
can then be estimated with a polynomial quantum speedup. Moreover, the quantum
PageRank state can be used in "q-sampling" protocols for testing properties of
distributions, which require exponentially fewer measurements than all
classical schemes designed for the same task. This can be used to decide
whether to run a classical update of the PageRank.Comment: 7 pages, 5 figures; closer to published versio
Grammar-Based Random Walkers in Semantic Networks
Semantic networks qualify the meaning of an edge relating any two vertices.
Determining which vertices are most "central" in a semantic network is
difficult because one relationship type may be deemed subjectively more
important than another. For this reason, research into semantic network metrics
has focused primarily on context-based rankings (i.e. user prescribed
contexts). Moreover, many of the current semantic network metrics rank semantic
associations (i.e. directed paths between two vertices) and not the vertices
themselves. This article presents a framework for calculating semantically
meaningful primary eigenvector-based metrics such as eigenvector centrality and
PageRank in semantic networks using a modified version of the random walker
model of Markov chain analysis. Random walkers, in the context of this article,
are constrained by a grammar, where the grammar is a user defined data
structure that determines the meaning of the final vertex ranking. The ideas in
this article are presented within the context of the Resource Description
Framework (RDF) of the Semantic Web initiative.Comment: First draft of manuscript originally written in November 200
Ranking Spaces for Predicting Human Movement in an Urban Environment
A city can be topologically represented as a connectivity graph, consisting
of nodes representing individual spaces and links if the corresponding spaces
are intersected. It turns out in the space syntax literature that some defined
topological metrics can capture human movement rates in individual spaces. In
other words, the topological metrics are significantly correlated to human
movement rates, and individual spaces can be ranked by the metrics for
predicting human movement. However, this correlation has never been well
justified. In this paper, we study the same issue by applying the weighted
PageRank algorithm to the connectivity graph or space-space topology for
ranking the individual spaces, and find surprisingly that (1) the PageRank
scores are better correlated to human movement rates than the space syntax
metrics, and (2) the underlying space-space topology demonstrates small world
and scale free properties. The findings provide a novel justification as to why
space syntax, or topological analysis in general, can be used to predict human
movement. We further conjecture that this kind of analysis is no more than
predicting a drunkard's walking on a small world and scale free network.
Keywords: Space syntax, topological analysis of networks, small world, scale
free, human movement, and PageRankComment: 11 pages, 5 figures, and 2 tables, English corrections from version 1
to version 2, major changes in the section of introduction from version 2 to
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