31,685 research outputs found
Multiplexity versus correlation: the role of local constraints in real multiplexes
Several real-world systems can be represented as multi-layer complex
networks, i.e. in terms of a superposition of various graphs, each related to a
different mode of connection between nodes. Hence, the definition of proper
mathematical quantities aiming at capturing the level of complexity of those
systems is required. Various attempts have been made to measure the empirical
dependencies between the layers of a multiplex, for both binary and weighted
networks. In the simplest case, such dependencies are measured via
correlation-based metrics: we show that this is equivalent to the use of
completely homogeneous benchmarks specifying only global constraints, such as
the total number of links in each layer. However, these approaches do not take
into account the heterogeneity in the degree and strength distributions, which
are instead a fundamental feature of real-world multiplexes. In this work, we
compare the observed dependencies between layers with the expected values
obtained from reference models that appropriately control for the observed
heterogeneity in the degree and strength distributions. This leads to novel
multiplexity measures that we test on different datasets, i.e. the
International Trade Network (ITN) and the European Airport Network (EAN). Our
findings confirm that the use of homogeneous benchmarks can lead to misleading
results, and furthermore highlight the important role played by the
distribution of hubs across layers.Comment: 32 pages, 6 figure
Analyzing covert social network foundation behind terrorism disaster
This paper addresses a method to analyze the covert social network foundation
hidden behind the terrorism disaster. It is to solve a node discovery problem,
which means to discover a node, which functions relevantly in a social network,
but escaped from monitoring on the presence and mutual relationship of nodes.
The method aims at integrating the expert investigator's prior understanding,
insight on the terrorists' social network nature derived from the complex graph
theory, and computational data processing. The social network responsible for
the 9/11 attack in 2001 is used to execute simulation experiment to evaluate
the performance of the method.Comment: 17pages, 10 figures, submitted to Int. J. Services Science
Variational principle for scale-free network motifs
For scale-free networks with degrees following a power law with an exponent
, the structures of motifs (small subgraphs) are not yet well
understood. We introduce a method designed to identify the dominant structure
of any given motif as the solution of an optimization problem. The unique
optimizer describes the degrees of the vertices that together span the most
likely motif, resulting in explicit asymptotic formulas for the motif count and
its fluctuations. We then classify all motifs into two categories: motifs with
small and large fluctuations
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
Robust causal structure learning with some hidden variables
We introduce a new method to estimate the Markov equivalence class of a
directed acyclic graph (DAG) in the presence of hidden variables, in settings
where the underlying DAG among the observed variables is sparse, and there are
a few hidden variables that have a direct effect on many of the observed ones.
Building on the so-called low rank plus sparse framework, we suggest a
two-stage approach which first removes the effect of the hidden variables, and
then estimates the Markov equivalence class of the underlying DAG under the
assumption that there are no remaining hidden variables. This approach is
consistent in certain high-dimensional regimes and performs favourably when
compared to the state of the art, both in terms of graphical structure recovery
and total causal effect estimation
Curvature of Co-Links Uncovers Hidden Thematic Layers in the World Wide Web
Beyond the information stored in pages of the World Wide Web, novel types of
``meta-information'' are created when they connect to each other. This
information is a collective effect of independent users writing and linking
pages, hidden from the casual user. Accessing it and understanding the
inter-relation of connectivity and content in the WWW is a challenging problem.
We demonstrate here how thematic relationships can be located precisely by
looking only at the graph of hyperlinks, gleaning content and context from the
Web without having to read what is in the pages. We begin by noting that
reciprocal links (co-links) between pages signal a mutual recognition of
authors, and then focus on triangles containing such links, since triangles
indicate a transitive relation. The importance of triangles is quantified by
the clustering coefficient (Watts) which we interpret as a curvature
(Gromov,Bridson-Haefliger). This defines a Web-landscape whose connected
regions of high curvature characterize a common topic. We show experimentally
that reciprocity and curvature, when combined, accurately capture this
meta-information for a wide variety of topics. As an example of future
directions we analyze the neural network of C. elegans (White, Wood), using the
same methods.Comment: 8 pages, 5 figures, expanded version of earlier submission with more
example
Riemannian-geometric entropy for measuring network complexity
A central issue of the science of complex systems is the quantitative
characterization of complexity. In the present work we address this issue by
resorting to information geometry. Actually we propose a constructive way to
associate to a - in principle any - network a differentiable object (a
Riemannian manifold) whose volume is used to define an entropy. The
effectiveness of the latter to measure networks complexity is successfully
proved through its capability of detecting a classical phase transition
occurring in both random graphs and scale--free networks, as well as of
characterizing small Exponential random graphs, Configuration Models and real
networks.Comment: 15 pages, 3 figure
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