25,248 research outputs found
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
We introduce the \texttt{pyunicorn} (Pythonic unified complex network and
recurrence analysis toolbox) open source software package for applying and
combining modern methods of data analysis and modeling from complex network
theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully
object-oriented and easily parallelizable package written in the language
Python. It allows for the construction of functional networks such as climate
networks in climatology or functional brain networks in neuroscience
representing the structure of statistical interrelationships in large data sets
of time series and, subsequently, investigating this structure using advanced
methods of complex network theory such as measures and models for spatial
networks, networks of interacting networks, node-weighted statistics or network
surrogates. Additionally, \texttt{pyunicorn} provides insights into the
nonlinear dynamics of complex systems as recorded in uni- and multivariate time
series from a non-traditional perspective by means of recurrence quantification
analysis (RQA), recurrence networks, visibility graphs and construction of
surrogate time series. The range of possible applications of the library is
outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure
Disentangling different types of El Ni\~no episodes by evolving climate network analysis
Complex network theory provides a powerful toolbox for studying the structure
of statistical interrelationships between multiple time series in various
scientific disciplines. In this work, we apply the recently proposed climate
network approach for characterizing the evolving correlation structure of the
Earth's climate system based on reanalysis data of surface air temperatures. We
provide a detailed study on the temporal variability of several global climate
network characteristics. Based on a simple conceptual view on red climate
networks (i.e., networks with a comparably low number of edges), we give a
thorough interpretation of our evolving climate network characteristics, which
allows a functional discrimination between recently recognized different types
of El Ni\~no episodes. Our analysis provides deep insights into the Earth's
climate system, particularly its global response to strong volcanic eruptions
and large-scale impacts of different phases of the El Ni\~no Southern
Oscillation (ENSO).Comment: 20 pages, 12 figure
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Bistability through triadic closure
We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean ļ¬eld theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure eļ¬ect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean ļ¬eld theory predicts bistable dynamics, and computational results conļ¬rm this prediction. We also discuss the calibration issue for a set of real cell phone data, and ļ¬nd support for a stratiļ¬ed model, where individuals are assigned to one of two distinct groups having diļ¬erent within-group and across-group dynamics
On the Role of Social Identity and Cohesion in Characterizing Online Social Communities
Two prevailing theories for explaining social group or community structure
are cohesion and identity. The social cohesion approach posits that social
groups arise out of an aggregation of individuals that have mutual
interpersonal attraction as they share common characteristics. These
characteristics can range from common interests to kinship ties and from social
values to ethnic backgrounds. In contrast, the social identity approach posits
that an individual is likely to join a group based on an intrinsic
self-evaluation at a cognitive or perceptual level. In other words group
members typically share an awareness of a common category membership.
In this work we seek to understand the role of these two contrasting theories
in explaining the behavior and stability of social communities in Twitter. A
specific focal point of our work is to understand the role of these theories in
disparate contexts ranging from disaster response to socio-political activism.
We extract social identity and social cohesion features-of-interest for large
scale datasets of five real-world events and examine the effectiveness of such
features in capturing behavioral characteristics and the stability of groups.
We also propose a novel measure of social group sustainability based on the
divergence in group discussion. Our main findings are: 1) Sharing of social
identities (especially physical location) among group members has a positive
impact on group sustainability, 2) Structural cohesion (represented by high
group density and low average shortest path length) is a strong indicator of
group sustainability, and 3) Event characteristics play a role in shaping group
sustainability, as social groups in transient events behave differently from
groups in events that last longer
Learning Opposites with Evolving Rules
The idea of opposition-based learning was introduced 10 years ago. Since then
a noteworthy group of researchers has used some notions of oppositeness to
improve existing optimization and learning algorithms. Among others,
evolutionary algorithms, reinforcement agents, and neural networks have been
reportedly extended into their opposition-based version to become faster and/or
more accurate. However, most works still use a simple notion of opposites,
namely linear (or type- I) opposition, that for each assigns its
opposite as . This, of course, is a very naive estimate of
the actual or true (non-linear) opposite , which has been
called type-II opposite in literature. In absence of any knowledge about a
function that we need to approximate, there seems to be no
alternative to the naivety of type-I opposition if one intents to utilize
oppositional concepts. But the question is if we can receive some level of
accuracy increase and time savings by using the naive opposite estimate
according to all reports in literature, what would we be able to
gain, in terms of even higher accuracies and more reduction in computational
complexity, if we would generate and employ true opposites? This work
introduces an approach to approximate type-II opposites using evolving fuzzy
rules when we first perform opposition mining. We show with multiple examples
that learning true opposites is possible when we mine the opposites from the
training data to subsequently approximate .Comment: Accepted for publication in The 2015 IEEE International Conference on
Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke
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