1,332,975 research outputs found
Complex network analysis and nonlinear dynamics
This chapter aims at reviewing complex network and nonlinear dynamical
models and methods that were either developed for or applied to socioeconomic
issues, and pertinent to the theme of New Economic Geography. After an introduction
to the foundations of the field of complex networks, the present summary
introduces some applications of complex networks to economics, finance, epidemic
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issue
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Complex networks
This chapter contains a brief introduction to complex networks, and in particular to small world and scale free networks. We show how to apply the replica method developed to analyse random matrices in statistical physics to calculate the spectral densities of the adjacency and Laplacian matrices of a scale free network. We use the effective medium approximation to treat networks with finite mean degree and discuss the local properties of random matrices associated with complex networks
Complex Networks
Introduction to the Special Issue on Complex Networks, Artificial Life
journal.Comment: 7 pages, in pres
Non-parametric Bayesian modeling of complex networks
Modeling structure in complex networks using Bayesian non-parametrics makes
it possible to specify flexible model structures and infer the adequate model
complexity from the observed data. This paper provides a gentle introduction to
non-parametric Bayesian modeling of complex networks: Using an infinite mixture
model as running example we go through the steps of deriving the model as an
infinite limit of a finite parametric model, inferring the model parameters by
Markov chain Monte Carlo, and checking the model's fit and predictive
performance. We explain how advanced non-parametric models for complex networks
can be derived and point out relevant literature
Complex networks analysis in socioeconomic models
This chapter aims at reviewing complex networks models and methods that were
either developed for or applied to socioeconomic issues, and pertinent to the
theme of New Economic Geography. After an introduction to the foundations of
the field of complex networks, the present summary adds insights on the
statistical mechanical approach, and on the most relevant computational aspects
for the treatment of these systems. As the most frequently used model for
interacting agent-based systems, a brief description of the statistical
mechanics of the classical Ising model on regular lattices, together with
recent extensions of the same model on small-world Watts-Strogatz and
scale-free Albert-Barabasi complex networks is included. Other sections of the
chapter are devoted to applications of complex networks to economics, finance,
spreading of innovations, and regional trade and developments. The chapter also
reviews results involving applications of complex networks to other relevant
socioeconomic issues, including results for opinion and citation networks.
Finally, some avenues for future research are introduced before summarizing the
main conclusions of the chapter.Comment: 39 pages, 185 references, (not final version of) a chapter prepared
for Complexity and Geographical Economics - Topics and Tools, P.
Commendatore, S.S. Kayam and I. Kubin Eds. (Springer, to be published
Using Artificial Intelligence in Wireless Sensor Routing Protocols
This paper represents a dissertation about how an artificial
intelligence technique can be applied to wireless sensor networks. Due
to the constraints on data processing and power consumption, the use
of artificial intelligence has been historically discarded in these kind of
networks. However, in some special scenarios the features of neural networks
are appropriate to develop complex tasks such as path discovery.
In this paper, we explore the performance of two very well known routing
paradigms, directed diffusion and Energy-Aware Routing, and our
routing algorithm, named SIR, which has the novelty of being based
on the introduction of neural networks in every sensor node. Extensive
simulations over our wireless sensor network simulator, OLIMPO, have
been carried out to study the efficiency of the introduction of neural networks.
A comparison of the results obtained with every routing protocol
is analyzed
An Introduction to Complex Networks in Climate Finance
In this perspective, we introduce recent research into the structure and function of complex investor networks supporting sustainability efforts. Using the case of solar, wind and hydro energy technologies, this perspective explores the complexity in low-carbon finance markets, defined as markets that direct capital flows towards low-carbon technologies, using network approaches to study their structure and dynamics. Investors are modeled as nodes which form a network or higher-order network connected by edges representing projects in which joint funding or security-related insurance was provided or other investment-related interaction occurred. We review the literature on investor networks generally, particularly in the case of complex networks, and address areas where these ideas were applied in this emerging field. The complex investor dynamics which emerge from the extant funding scenarios are not well understood. These dynamics have the potential to result in interesting non-linear behaviour, growth, and decline, which can be studied, explained and controlled using the tools of network science
Localization transition on complex networks via spectral statistics
The spectral statistics of complex networks are numerically studied.
The features of the Anderson metal-insulator transition are found to be
similar for a wide range of different networks. A metal-insulator transition as
a function of the disorder can be observed for different classes of complex
networks for which the average connectivity is small. The critical index of the
transition corresponds to the mean field expectation. When the connectivity is
higher, the amount of disorder needed to reach a certain degree of localization
is proportional to the average connectivity, though a precise transition cannot
be identified. The absence of a clear transition at high connectivity is
probably due to the very compact structure of the highly connected networks,
resulting in a small diameter even for a large number of sites.Comment: 6 pages, expanded introduction and referencess (to appear in PRE
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