40,834 research outputs found
Forman-Ricci flow for change detection in large dynamic data sets
We present a viable solution to the challenging question of change detection
in complex networks inferred from large dynamic data sets. Building on Forman's
discretization of the classical notion of Ricci curvature, we introduce a novel
geometric method to characterize different types of real-world networks with an
emphasis on peer-to-peer networks. Furthermore we adapt the classical Ricci
flow that already proved to be a powerful tool in image processing and
graphics, to the case of undirected and weighted networks. The application of
the proposed method on peer-to-peer networks yields insights into topological
properties and the structure of their underlying data.Comment: Conference paper, accepted at ICICS 2016. (Updated version
Causal connectivity of evolved neural networks during behavior
To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics
Complex Network Tools to Understand the Behavior of Criminality in Urban Areas
Complex networks are nowadays employed in several applications. Modeling
urban street networks is one of them, and in particular to analyze criminal
aspects of a city. Several research groups have focused on such application,
but until now, there is a lack of a well-defined methodology for employing
complex networks in a whole crime analysis process, i.e. from data preparation
to a deep analysis of criminal communities. Furthermore, the "toolset"
available for those works is not complete enough, also lacking techniques to
maintain up-to-date, complete crime datasets and proper assessment measures. In
this sense, we propose a threefold methodology for employing complex networks
in the detection of highly criminal areas within a city. Our methodology
comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community
Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of
assessment measures for analyzing intrinsic criminality of communities,
especially when considering different crime types. We show our methodology by
applying it to a real crime dataset from the city of San Francisco - CA, USA.
The results confirm its effectiveness to identify and analyze high criminality
areas within a city. Hence, our contributions provide a basis for further
developments on complex networks applied to crime analysis.Comment: 7 pages, 2 figures, 14th International Conference on Information
Technology : New Generation
Statistical mechanics of the international trade network
Analyzing real data on international trade covering the time interval
1950-2000, we show that in each year over the analyzed period the network is a
typical representative of the ensemble of maximally random weighted networks,
whose directed connections (bilateral trade volumes) are only characterized by
the product of the trading countries' GDPs. It means that time evolution of
this network may be considered as a continuous sequence of equilibrium states,
i.e. quasi-static process. This, in turn, allows one to apply the linear
response theory to make (and also verify) simple predictions about the network.
In particular, we show that bilateral trade fulfills fluctuation-response
theorem, which states that the average relative change in import (export)
between two countries is a sum of relative changes in their GDPs. Yearly
changes in trade volumes prove that the theorem is valid.Comment: 6 pages, 2 figure
Analyzing Ideological Communities in Congressional Voting Networks
We here study the behavior of political party members aiming at identifying
how ideological communities are created and evolve over time in diverse
(fragmented and non-fragmented) party systems. Using public voting data of both
Brazil and the US, we propose a methodology to identify and characterize
ideological communities, their member polarization, and how such communities
evolve over time, covering a 15-year period. Our results reveal very distinct
patterns across the two case studies, in terms of both structural and dynamic
properties
Exact results of the limited penetrable horizontal visibility graph associated to random time series and its application
The limited penetrable horizontal visibility algorithm is a new time analysis
tool and is a further development of the horizontal visibility algorithm. We
present some exact results on the topological properties of the limited
penetrable horizontal visibility graph associated with random series. We show
that the random series maps on a limited penetrable horizontal visibility graph
with exponential degree distribution ,
independent of the probability distribution from which the series was
generated. We deduce the exact expressions of the mean degree and the
clustering coefficient and demonstrate the long distance visibility property.
Numerical simulations confirm the accuracy of our theoretical results. We then
examine several deterministic chaotic series (a logistic map, the
Hnon map, the Lorentz system, and an energy price chaotic system)
and a real crude oil price series to test our results. The empirical results
show that the limited penetrable horizontal visibility algorithm is direct, has
a low computational cost when discriminating chaos from uncorrelated
randomness, and is able to measure the global evolution characteristics of the
real time series.Comment: 23 pages, 12 figure
- …