2,539 research outputs found
Reconstructing the world trade multiplex: the role of intensive and extensive biases
In economic and financial networks, the strength of each node has always an
important economic meaning, such as the size of supply and demand, import and
export, or financial exposure. Constructing null models of networks matching
the observed strengths of all nodes is crucial in order to either detect
interesting deviations of an empirical network from economically meaningful
benchmarks or reconstruct the most likely structure of an economic network when
the latter is unknown. However, several studies have proved that real economic
networks and multiplexes are topologically very different from configurations
inferred only from node strengths. Here we provide a detailed analysis of the
World Trade Multiplex by comparing it to an enhanced null model that
simultaneously reproduces the strength and the degree of each node. We study
several temporal snapshots and almost one hundred layers (commodity classes) of
the multiplex and find that the observed properties are systematically well
reproduced by our model. Our formalism allows us to introduce the (static)
concept of extensive and intensive bias, defined as a measurable tendency of
the network to prefer either the formation of extra links or the reinforcement
of link weights, with respect to a reference case where only strengths are
enforced. Our findings complement the existing economic literature on (dynamic)
intensive and extensive trade margins. More in general, they show that
real-world multiplexes can be strongly shaped by layer-specific local
constraints
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
Inference of hidden structures in complex physical systems by multi-scale clustering
We survey the application of a relatively new branch of statistical
physics--"community detection"-- to data mining. In particular, we focus on the
diagnosis of materials and automated image segmentation. Community detection
describes the quest of partitioning a complex system involving many elements
into optimally decoupled subsets or communities of such elements. We review a
multiresolution variant which is used to ascertain structures at different
spatial and temporal scales. Significant patterns are obtained by examining the
correlations between different independent solvers. Similar to other
combinatorial optimization problems in the NP complexity class, community
detection exhibits several phases. Typically, illuminating orders are revealed
by choosing parameters that lead to extremal information theory correlations.Comment: 25 pages, 16 Figures; a review of earlier work
Community Detection in Multiplex Networks
A multiplex network models different modes of interaction among same-type
entities. In this article we provide a taxonomy of community detection
algorithms in multiplex networks. We characterize the different algorithms
based on various properties and we discuss the type of communities detected by
each method. We then provide an extensive experimental evaluation of the
reviewed methods to answer three main questions: to what extent the evaluated
methods are able to detect ground-truth communities, to what extent different
methods produce similar community structures and to what extent the evaluated
methods are scalable. One goal of this survey is to help scholars and
practitioners to choose the right methods for the data and the task at hand,
while also emphasizing when such choice is problematic.Comment: 55 pages. Accepted for publication on ACM Computing Surveys in a
shorter versio
Topics in social network analysis and network science
This chapter introduces statistical methods used in the analysis of social
networks and in the rapidly evolving parallel-field of network science.
Although several instances of social network analysis in health services
research have appeared recently, the majority involve only the most basic
methods and thus scratch the surface of what might be accomplished.
Cutting-edge methods using relevant examples and illustrations in health
services research are provided
Post-processing partitions to identify domains of modularity optimization
We introduce the Convex Hull of Admissible Modularity Partitions (CHAMP)
algorithm to prune and prioritize different network community structures
identified across multiple runs of possibly various computational heuristics.
Given a set of partitions, CHAMP identifies the domain of modularity
optimization for each partition ---i.e., the parameter-space domain where it
has the largest modularity relative to the input set---discarding partitions
with empty domains to obtain the subset of partitions that are "admissible"
candidate community structures that remain potentially optimal over indicated
parameter domains. Importantly, CHAMP can be used for multi-dimensional
parameter spaces, such as those for multilayer networks where one includes a
resolution parameter and interlayer coupling. Using the results from CHAMP, a
user can more appropriately select robust community structures by observing the
sizes of domains of optimization and the pairwise comparisons between
partitions in the admissible subset. We demonstrate the utility of CHAMP with
several example networks. In these examples, CHAMP focuses attention onto
pruned subsets of admissible partitions that are 20-to-1785 times smaller than
the sets of unique partitions obtained by community detection heuristics that
were input into CHAMP.Comment: http://www.mdpi.com/1999-4893/10/3/9
Dynamical Systems on Networks: A Tutorial
We give a tutorial for the study of dynamical systems on networks. We focus
especially on "simple" situations that are tractable analytically, because they
can be very insightful and provide useful springboards for the study of more
complicated scenarios. We briefly motivate why examining dynamical systems on
networks is interesting and important, and we then give several fascinating
examples and discuss some theoretical results. We also briefly discuss
dynamical systems on dynamical (i.e., time-dependent) networks, overview
software implementations, and give an outlook on the field.Comment: 39 pages, 1 figure, submitted, more examples and discussion than
original version, some reorganization and also more pointers to interesting
direction
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