110,501 research outputs found
Optimal scales in weighted networks
The analysis of networks characterized by links with heterogeneous intensity
or weight suffers from two long-standing problems of arbitrariness. On one
hand, the definitions of topological properties introduced for binary graphs
can be generalized in non-unique ways to weighted networks. On the other hand,
even when a definition is given, there is no natural choice of the (optimal)
scale of link intensities (e.g. the money unit in economic networks). Here we
show that these two seemingly independent problems can be regarded as
intimately related, and propose a common solution to both. Using a formalism
that we recently proposed in order to map a weighted network to an ensemble of
binary graphs, we introduce an information-theoretic approach leading to the
least biased generalization of binary properties to weighted networks, and at
the same time fixing the optimal scale of link intensities. We illustrate our
method on various social and economic networks.Comment: Accepted for presentation at SocInfo 2013, Kyoto, 25-27 November 2013
(http://www.socinfo2013.org
Optimal Path and Minimal Spanning Trees in Random Weighted Networks
We review results on the scaling of the optimal path length in random
networks with weighted links or nodes. In strong disorder we find that the
length of the optimal path increases dramatically compared to the known small
world result for the minimum distance. For Erd\H{o}s-R\'enyi (ER) and scale
free networks (SF), with parameter (), we find that the
small-world nature is destroyed. We also find numerically that for weak
disorder the length of the optimal path scales logaritmically with the size of
the networks studied. We also review the transition between the strong and weak
disorder regimes in the scaling properties of the length of the optimal path
for ER and SF networks and for a general distribution of weights, and suggest
that for any distribution of weigths, the distribution of optimal path lengths
has a universal form which is controlled by the scaling parameter
where plays the role of the disorder strength, and
is the length of the optimal path in strong disorder. The
relation for is derived analytically and supported by numerical
simulations. We then study the minimum spanning tree (MST) and show that it is
composed of percolation clusters, which we regard as "super-nodes", connected
by a scale-free tree. We furthermore show that the MST can be partitioned into
two distinct components. One component the {\it superhighways}, for which the
nodes with high centrality dominate, corresponds to the largest cluster at the
percolation threshold which is a subset of the MST. In the other component,
{\it roads}, low centrality nodes dominate. We demonstrate the significance
identifying the superhighways by showing that one can improve significantly the
global transport by improving a very small fraction of the network.Comment: review, accepted at IJB
Encoding dynamics for multiscale community detection: Markov time sweeping for the Map equation
The detection of community structure in networks is intimately related to
finding a concise description of the network in terms of its modules. This
notion has been recently exploited by the Map equation formalism (M. Rosvall
and C.T. Bergstrom, PNAS, 105(4), pp.1118--1123, 2008) through an
information-theoretic description of the process of coding inter- and
intra-community transitions of a random walker in the network at stationarity.
However, a thorough study of the relationship between the full Markov dynamics
and the coding mechanism is still lacking. We show here that the original Map
coding scheme, which is both block-averaged and one-step, neglects the internal
structure of the communities and introduces an upper scale, the `field-of-view'
limit, in the communities it can detect. As a consequence, Map is well tuned to
detect clique-like communities but can lead to undesirable overpartitioning
when communities are far from clique-like. We show that a signature of this
behavior is a large compression gap: the Map description length is far from its
ideal limit. To address this issue, we propose a simple dynamic approach that
introduces time explicitly into the Map coding through the analysis of the
weighted adjacency matrix of the time-dependent multistep transition matrix of
the Markov process. The resulting Markov time sweeping induces a dynamical
zooming across scales that can reveal (potentially multiscale) community
structure above the field-of-view limit, with the relevant partitions indicated
by a small compression gap.Comment: 10 pages, 6 figure
Laplacian Dynamics and Multiscale Modular Structure in Networks
Most methods proposed to uncover communities in complex networks rely on
their structural properties. Here we introduce the stability of a network
partition, a measure of its quality defined in terms of the statistical
properties of a dynamical process taking place on the graph. The time-scale of
the process acts as an intrinsic parameter that uncovers community structures
at different resolutions. The stability extends and unifies standard notions
for community detection: modularity and spectral partitioning can be seen as
limiting cases of our dynamic measure. Similarly, recently proposed
multi-resolution methods correspond to linearisations of the stability at short
times. The connection between community detection and Laplacian dynamics
enables us to establish dynamically motivated stability measures linked to
distinct null models. We apply our method to find multi-scale partitions for
different networks and show that the stability can be computed efficiently for
large networks with extended versions of current algorithms.Comment: New discussions on the selection of the most significant scales and
the generalisation of stability to directed network
Distributed flow optimization and cascading effects in weighted complex networks
We investigate the effect of a specific edge weighting scheme on distributed flow efficiency and robustness to cascading
failures in scale-free networks. In particular, we analyze a simple, yet
fundamental distributed flow model: current flow in random resistor networks.
By the tuning of control parameter and by considering two general cases
of relative node processing capabilities as well as the effect of bandwidth, we
show the dependence of transport efficiency upon the correlations between the
topology and weights. By studying the severity of cascades for different
control parameter , we find that network resilience to cascading
overloads and network throughput is optimal for the same value of over
the range of node capacities and available bandwidth
Optimal Paths in Complex Networks with Correlated Weights: The World-wide Airport Network
We study complex networks with weights, , associated with each link
connecting node and . The weights are chosen to be correlated with the
network topology in the form found in two real world examples, (a) the
world-wide airport network, and (b) the {\it E. Coli} metabolic network. Here
, where and are the degrees of
nodes and , is a random number and represents the
strength of the correlations. The case represents correlation
between weights and degree, while represents anti-correlation and
the case reduces to the case of no correlations. We study the
scaling of the lengths of the optimal paths, , with the system
size in strong disorder for scale-free networks for different . We
calculate the robustness of correlated scale-free networks with different
, and find the networks with to be the most robust
networks when compared to the other values of . We propose an
analytical method to study percolation phenomena on networks with this kind of
correlation. We compare our simulation results with the real world-wide airport
network, and we find good agreement
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