42,777 research outputs found
Measuring and mitigating behavioural segregation using Call Detail Records
The overwhelming amounts of data we generate in our daily routine and in social networks has been crucial for the understanding of various social and economic factors. The use of this data represents a low-cost alternative source of information in parallel to census data and surveys. Here, we advocate for such an approach to assess and alleviate the segregation of Syrian refugees in Turkey. Using a large dataset of mobile phone records provided by Turkey's largest mobile phone service operator, Türk Telekom, in the frame of the Data 4 Refugees project, we define, analyse and optimise inter-group integration as it relates to the communication patterns of two segregated populations: refugees living in Turkey and the local Turkish population. Our main hypothesis is that making these two communities more similar (in our case, in terms of behaviour) may increase the level of positive exposure between them, due to the well-known sociological principle of homophily. To achieve this, working from the records of call and SMS origins and destinations between and among both populations, we develop an extensible, statistically-solid, and reliable framework to measure the differences between the communication patterns of two groups. In order to show the applicability of our framework, we assess how house mixing strategies, in combination with public and private investment, may help to overcome segregation. We first identify the districts of the Istanbul province where refugees and local population communication patterns differ in order to then utilise our framework to improve the situation. Our results show potential in this regard, as we observe a significant reduction of segregation while limiting, in turn, the consequences in terms of rent increase
Network segregation in a model of misinformation and fact checking
Misinformation under the form of rumor, hoaxes, and conspiracy theories
spreads on social media at alarming rates. One hypothesis is that, since social
media are shaped by homophily, belief in misinformation may be more likely to
thrive on those social circles that are segregated from the rest of the
network. One possible antidote is fact checking which, in some cases, is known
to stop rumors from spreading further. However, fact checking may also backfire
and reinforce the belief in a hoax. Here we take into account the combination
of network segregation, finite memory and attention, and fact-checking efforts.
We consider a compartmental model of two interacting epidemic processes over a
network that is segregated between gullible and skeptic users. Extensive
simulation and mean-field analysis show that a more segregated network
facilitates the spread of a hoax only at low forgetting rates, but has no
effect when agents forget at faster rates. This finding may inform the
development of mitigation techniques and overall inform on the risks of
uncontrolled misinformation online
Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network
We introduce an easily computable topological measure which locates the
effective crossover between segregation and integration in a modular network.
Segregation corresponds to the degree of network modularity, while integration
is expressed in terms of the algebraic connectivity of an associated
hyper-graph. The rigorous treatment of the simplified case of cliques of equal
size that are gradually rewired until they become completely merged, allows us
to show that this topological crossover can be made to coincide with a
dynamical crossover from cluster to global synchronization of a system of
coupled phase oscillators. The dynamical crossover is signaled by a peak in the
product of the measures of intra-cluster and global synchronization, which we
propose as a dynamical measure of complexity. This quantity is much easier to
compute than the entropy (of the average frequencies of the oscillators), and
displays a behavior which closely mimics that of the dynamical complexity index
based on the latter. The proposed toplogical measure simultaneously provides
information on the dynamical behavior, sheds light on the interplay between
modularity vs total integration and shows how this affects the capability of
the network to perform both local and distributed dynamical tasks
Characterization of the Community Structure of Large Scale Functional Brain Networks During Ketamine-Medetomidine Anesthetic Induction
One of the central questions in neuroscience is to understand the way
communication is organized in the brain, trying to comprehend how cognitive
capacities or physiological states of the organism are potentially related to
brain activities involving interactions of several brain areas. One important
characteristic of the functional brain networks is that they are modularly
structured, being this modular architecture regarded to account for a series of
properties and functional dynamics. In the neurobiological context, communities
may indicate brain regions that are involved in one same activity, representing
neural segregated processes. Several studies have demonstrated the modular
character of organization of brain activities. However, empirical evidences
regarding to its dynamics and relation to different levels of consciousness
have not been reported yet. Within this context, this research sought to
characterize the community structure of functional brain networks during an
anesthetic induction process. The experiment was based on intra-cranial
recordings of neural activities of an old world macaque of the species Macaca
fuscata during a Ketamine-Medetomidine anesthetic induction process. Networks
were serially estimated in time intervals of five seconds. Changes were
observed within about one and a half minutes after the administration of the
anesthetics, revealing the occurrence of a transition on the community
structure. The awake state was characterized by the presence of large clusters
involving frontal and parietal regions, while the anesthetized state by the
presence of communities in the primary visual and motor cortices, being the
areas of the secondary associative cortex most affected. The results report the
influence of general anesthesia on the structure of functional clusters,
contributing for understanding some new aspects of neural correlates of
consciousness.Comment: 24 pages, 8 figures. arXiv admin note: text overlap with
arXiv:1604.0000
Influence of wiring cost on the large-scale architecture of human cortical connectivity
In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain
Cumulative Risk and a Call for Action in Environmental Justice Communities
Health disparities, social inequalities, and environmental injustice cumulatively affect individual and community vulnerability and overall health; yet health researchers, social scientists and environmental scientists generally study them separately. Cumulative risk assessment in poor, racially segregated, economically isolated and medically underserved communities needs to account for their multiple layers of vulnerability, including greater susceptibility, greater exposure, less preparedness to cope, and less ability to recover in the face of exposure. Recommendations for evidence-based action in environmental justice communities include: reducing pollution in communities of highest burden; building on community resources; redressing inequality when doing community-based research; and creating a screening framework to identify communities of greatest risk
Partisan Asymmetries in Online Political Activity
We examine partisan differences in the behavior, communication patterns and
social interactions of more than 18,000 politically-active Twitter users to
produce evidence that points to changing levels of partisan engagement with the
American online political landscape. Analysis of a network defined by the
communication activity of these users in proximity to the 2010 midterm
congressional elections reveals a highly segregated, well clustered partisan
community structure. Using cluster membership as a high-fidelity (87% accuracy)
proxy for political affiliation, we characterize a wide range of differences in
the behavior, communication and social connectivity of left- and right-leaning
Twitter users. We find that in contrast to the online political dynamics of the
2008 campaign, right-leaning Twitter users exhibit greater levels of political
activity, a more tightly interconnected social structure, and a communication
network topology that facilitates the rapid and broad dissemination of
political information.Comment: 17 pages, 10 figures, 6 table
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