10,894 research outputs found
Interbank markets and multiplex networks: centrality measures and statistical null models
The interbank market is considered one of the most important channels of
contagion. Its network representation, where banks and claims/obligations are
represented by nodes and links (respectively), has received a lot of attention
in the recent theoretical and empirical literature, for assessing systemic risk
and identifying systematically important financial institutions. Different
types of links, for example in terms of maturity and collateralization of the
claim/obligation, can be established between financial institutions. Therefore
a natural representation of the interbank structure which takes into account
more features of the market, is a multiplex, where each layer is associated
with a type of link. In this paper we review the empirical structure of the
multiplex and the theoretical consequences of this representation. We also
investigate the betweenness and eigenvector centrality of a bank in the
network, comparing its centrality properties across different layers and with
Maximum Entropy null models.Comment: To appear in the book "Interconnected Networks", A. Garas e F.
Schweitzer (eds.), Springer Complexity Serie
Multiplexity and multireciprocity in directed multiplexes
Real-world multi-layer networks feature nontrivial dependencies among links
of different layers. Here we argue that, if links are directed, dependencies
are twofold. Besides the ordinary tendency of links of different layers to
align as the result of `multiplexity', there is also a tendency to anti-align
as the result of what we call `multireciprocity', i.e. the fact that links in
one layer can be reciprocated by \emph{opposite} links in a different layer.
Multireciprocity generalizes the scalar definition of single-layer reciprocity
to that of a square matrix involving all pairs of layers. We introduce
multiplexity and multireciprocity matrices for both binary and weighted
multiplexes and validate their statistical significance against maximum-entropy
null models that filter out the effects of node heterogeneity. We then perform
a detailed empirical analysis of the World Trade Multiplex (WTM), representing
the import-export relationships between world countries in different
commodities. We show that the WTM exhibits strong multiplexity and
multireciprocity, an effect which is however largely encoded into the degree or
strength sequences of individual layers. The residual effects are still
significant and allow to classify pairs of commodities according to their
tendency to be traded together in the same direction and/or in opposite ones.
We also find that the multireciprocity of the WTM is significantly lower than
the usual reciprocity measured on the aggregate network. Moreover, layers with
low (high) internal reciprocity are embedded within sets of layers with
comparably low (high) mutual multireciprocity. This suggests that, in the WTM,
reciprocity is inherent to groups of related commodities rather than to
individual commodities. We discuss the implications for international trade
research focusing on product taxonomies, the product space, and
fitness/complexity metrics.Comment: 20 pages, 8 figure
Entropy concepts and DNA investigations
Topological and metric entropies of the DNA sequences from different
organisms were calculated. Obtained results were compared each other and with
ones of corresponding artificial sequences. For all envisaged DNA sequences
there is a maximum of heterogeneity. It falls in the block length interval
[5,7].
Maximum distinction between natural and artificial sequences is shifted on
1-3 position from the maximum of heterogeneity to the right as for metric as
for topological entropy. This point on the specificity of real DNA sequences in
the interval.Comment: 10 pages 7 figures submitted to PL
Agents, Bookmarks and Clicks: A topical model of Web traffic
Analysis of aggregate and individual Web traffic has shown that PageRank is a
poor model of how people navigate the Web. Using the empirical traffic patterns
generated by a thousand users, we characterize several properties of Web
traffic that cannot be reproduced by Markovian models. We examine both
aggregate statistics capturing collective behavior, such as page and link
traffic, and individual statistics, such as entropy and session size. No model
currently explains all of these empirical observations simultaneously. We show
that all of these traffic patterns can be explained by an agent-based model
that takes into account several realistic browsing behaviors. First, agents
maintain individual lists of bookmarks (a non-Markovian memory mechanism) that
are used as teleportation targets. Second, agents can retreat along visited
links, a branching mechanism that also allows us to reproduce behaviors such as
the use of a back button and tabbed browsing. Finally, agents are sustained by
visiting novel pages of topical interest, with adjacent pages being more
topically related to each other than distant ones. This modulates the
probability that an agent continues to browse or starts a new session, allowing
us to recreate heterogeneous session lengths. The resulting model is capable of
reproducing the collective and individual behaviors we observe in the empirical
data, reconciling the narrowly focused browsing patterns of individual users
with the extreme heterogeneity of aggregate traffic measurements. This result
allows us to identify a few salient features that are necessary and sufficient
to interpret the browsing patterns observed in our data. In addition to the
descriptive and explanatory power of such a model, our results may lead the way
to more sophisticated, realistic, and effective ranking and crawling
algorithms.Comment: 10 pages, 16 figures, 1 table - Long version of paper to appear in
Proceedings of the 21th ACM conference on Hypertext and Hypermedi
Increased signaling entropy in cancer requires the scale-free property of protein interaction networks
One of the key characteristics of cancer cells is an increased phenotypic
plasticity, driven by underlying genetic and epigenetic perturbations. However,
at a systems-level it is unclear how these perturbations give rise to the
observed increased plasticity. Elucidating such systems-level principles is key
for an improved understanding of cancer. Recently, it has been shown that
signaling entropy, an overall measure of signaling pathway promiscuity, and
computable from integrating a sample's gene expression profile with a protein
interaction network, correlates with phenotypic plasticity and is increased in
cancer compared to normal tissue. Here we develop a computational framework for
studying the effects of network perturbations on signaling entropy. We
demonstrate that the increased signaling entropy of cancer is driven by two
factors: (i) the scale-free (or near scale-free) topology of the interaction
network, and (ii) a subtle positive correlation between differential gene
expression and node connectivity. Indeed, we show that if protein interaction
networks were random graphs, described by Poisson degree distributions, that
cancer would generally not exhibit an increased signaling entropy. In summary,
this work exposes a deep connection between cancer, signaling entropy and
interaction network topology.Comment: 20 pages, 5 figures. In Press in Sci Rep 201
Quantifying the interdisciplinarity of scientific journals and fields
There is an overall perception of increased interdisciplinarity in science,
but this is difficult to confirm quantitatively owing to the lack of adequate
methods to evaluate subjective phenomena. This is no different from the
difficulties in establishing quantitative relationships in human and social
sciences. In this paper we quantified the interdisciplinarity of scientific
journals and science fields by using an entropy measurement based on the
diversity of the subject categories of journals citing a specific journal. The
methodology consisted in building citation networks using the Journal Citation
Reports database, in which the nodes were journals and edges were established
based on citations among journals. The overall network for the 11-year period
(1999-2009) studied was small-world and scale free with regard to the
in-strength. Upon visualizing the network topology an overall structure of the
various science fields could be inferred, especially their interconnections. We
confirmed quantitatively that science fields are becoming increasingly
interdisciplinary, with the degree of interdisplinarity (i.e. entropy)
correlating strongly with the in-strength of journals and with the impact
factor.Comment: 23 pages, 6 figure
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