16,543 research outputs found
The envirome and the connectome: exploring the structural noise in the human brain associated with socioeconomic deprivation
Complex cognitive functions are widely recognized to be the result of a number of brain regions working together as large-scale networks. Recently, complex network analysis has been used to characterize various structural properties of the large scale network organization of the brain. For example, the human brain has been found to have a modular architecture i.e. regions within the network form communities (modules) with more connections between regions within the community compared to regions outside it. The aim of this study was to examine the modular and overlapping modular architecture of the brain networks using complex network analysis. We also examined the association between neighborhood level deprivation and brain network structure – modularity and grey nodes. We compared network structure derived from anatomical MRI scans of 42 middle-aged neurologically healthy men from the least (LD) and the most deprived (MD) neighborhoods of Glasgow with their corresponding random networks. Cortical morphological covariance networks were constructed from the cortical thickness derived from the MRI scans of the brain. For a given modularity threshold, networks derived from the MD group showed similar number of modules compared to their corresponding random networks, while networks derived from the LD group had more modules compared to their corresponding random networks. The MD group also had fewer grey nodes – a measure of overlapping modular structure. These results suggest that apparent structural difference in brain networks may be driven by differences in cortical thicknesses between groups. This demonstrates a structural organization that is consistent with a system that is less robust and less efficient in information processing. These findings provide some evidence of the relationship between socioeconomic deprivation and brain network topology
Dynamic Social Balance and Convergent Appraisals via Homophily and Influence Mechanisms
Social balance theory describes allowable and forbidden configurations of the
topologies of signed directed social appraisal networks. In this paper, we
propose two discrete-time dynamical systems that explain how an appraisal
network \textcolor{blue}{converges to} social balance from an initially
unbalanced configuration. These two models are based on two different
socio-psychological mechanisms respectively: the homophily mechanism and the
influence mechanism. Our main theoretical contribution is a comprehensive
analysis for both models in three steps. First, we establish the well-posedness
and bounded evolution of the interpersonal appraisals. Second, we fully
characterize the set of equilibrium points; for both models, each equilibrium
network is composed by an arbitrary number of complete subgraphs satisfying
structural balance. Third, we establish the equivalence among three distinct
properties: non-vanishing appraisals, convergence to all-to-all appraisal
networks, and finite-time achievement of social balance. In addition to
theoretical analysis, Monte Carlo validations illustrates how the non-vanishing
appraisal condition holds for generic initial conditions in both models.
Moreover, numerical comparison between the two models indicate that the
homophily-based model might be a more universal explanation for the formation
of social balance. Finally, adopting the homophily-based model, we present
numerical results on the mediation and globalization of local conflicts, the
competition for allies, and the asymptotic formation of a single versus two
factions
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Different perceptions of social dilemmas: Evolutionary multigames in structured populations
Motivated by the fact that the same social dilemma can be perceived
differently by different players, we here study evolutionary multigames in
structured populations. While the core game is the weak prisoner's dilemma, a
fraction of the population adopts either a positive or a negative value of the
sucker's payoff, thus playing either the traditional prisoner's dilemma or the
snowdrift game. We show that the higher the fraction of the population adopting
a different payoff matrix, the more the evolution of cooperation is promoted.
The microscopic mechanism responsible for this outcome is unique to structured
populations, and it is due to the payoff heterogeneity, which spontaneously
introduces strong cooperative leaders that give rise to an asymmetric strategy
imitation flow in favor of cooperation. We demonstrate that the reported
evolutionary outcomes are robust against variations of the interaction network,
and they also remain valid if players are allowed to vary which game they play
over time. These results corroborate existing evidence in favor of
heterogeneity-enhanced network reciprocity, and they reveal how different
perceptions of social dilemmas may contribute to their resolution.Comment: 7 two-column pages, 5 figures; accepted for publication in Physical
Review
Defecting or not defecting: how to "read" human behavior during cooperative games by EEG measurements
Understanding the neural mechanisms responsible for human social interactions
is difficult, since the brain activities of two or more individuals have to be
examined simultaneously and correlated with the observed social patterns. We
introduce the concept of hyper-brain network, a connectivity pattern
representing at once the information flow among the cortical regions of a
single brain as well as the relations among the areas of two distinct brains.
Graph analysis of hyper-brain networks constructed from the EEG scanning of 26
couples of individuals playing the Iterated Prisoner's Dilemma reveals the
possibility to predict non-cooperative interactions during the decision-making
phase. The hyper-brain networks of two-defector couples have significantly less
inter-brain links and overall higher modularity - i.e. the tendency to form two
separate subgraphs - than couples playing cooperative or tit-for-tat
strategies. The decision to defect can be "read" in advance by evaluating the
changes of connectivity pattern in the hyper-brain network
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
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