485 research outputs found
Phase transitions in social sciences: two-populations mean field theory
A new mean field statistical mechanics model of two interacting groups of
spins is introduced and the phase transition studied in terms of their relative
size. A jump of the average magnetization is found for large values of the
mutual interaction when the relative percentage of the two populations crosses
a critical threshold. It is shown how the critical percentage depends on
internal interactions and on the initial magnetizations. The model is
interpreted as a prototype of resident-immigrant cultural interaction and
conclusions from the social sciences perspectives are drawn
Generalized Master Equations for Non-Poisson Dynamics on Networks
The traditional way of studying temporal networks is to aggregate the
dynamics of the edges to create a static weighted network. This implicitly
assumes that the edges are governed by Poisson processes, which is not
typically the case in empirical temporal networks. Consequently, we examine the
effects of non-Poisson inter-event statistics on the dynamics of edges, and we
apply the concept of a generalized master equation to the study of
continuous-time random walks on networks. We show that the equation reduces to
the standard rate equations when the underlying process is Poisson and that the
stationary solution is determined by an effective transition matrix whose
leading eigenvector is easy to calculate. We discuss the implications of our
work for dynamical processes on temporal networks and for the construction of
network diagnostics that take into account their nontrivial stochastic nature
Universality in movie rating distributions
In this paper histograms of user ratings for movies (1,...,10) are analysed.
The evolving stabilised shapes of histograms follow the rule that all are
either double- or triple-peaked. Moreover, at most one peak can be on the
central bins 2,...,9 and the distribution in these bins looks smooth
`Gaussian-like' while changes at the extremes (1 and 10) often look abrupt. It
is shown that this is well approximated under the assumption that histograms
are confined and discretised probability density functions of L\'evy skew
alpha-stable distributions. These distributions are the only stable
distributions which could emerge due to a generalized central limit theorem
from averaging of various independent random avriables as which one can see the
initial opinions of users. Averaging is also an appropriate assumption about
the social process which underlies the process of continuous opinion formation.
Surprisingly, not the normal distribution achieves the best fit over histograms
obseved on the web, but distributions with fat tails which decay as power-laws
with exponent -(1+alpha) (alpha=4/3). The scale and skewness parameters of the
Levy skew alpha-stable distributions seem to depend on the deviation from an
average movie (with mean about 7.6). The histogram of such an average movie has
no skewness and is the most narrow one. If a movie deviates from average the
distribution gets broader and skew. The skewness pronounces the deviation. This
is used to construct a one parameter fit which gives some evidence of
universality in processes of continuous opinion dynamics about taste.Comment: 8 pages, 5 figures, accepted for publicatio
Collective Decision Dynamics in the Presence of External Drivers
We develop a sequence of models describing information transmission and
decision dynamics for a network of individual agents subject to multiple
sources of influence. Our general framework is set in the context of an
impending natural disaster, where individuals, represented by nodes on the
network, must decide whether or not to evacuate. Sources of influence include a
one-to-many externally driven global broadcast as well as pairwise
interactions, across links in the network, in which agents transmit either
continuous opinions or binary actions. We consider both uniform and variable
threshold rules on the individual opinion as baseline models for
decision-making. Our results indicate that 1) social networks lead to
clustering and cohesive action among individuals, 2) binary information
introduces high temporal variability and stagnation, and 3) information
transmission over the network can either facilitate or hinder action adoption,
depending on the influence of the global broadcast relative to the social
network. Our framework highlights the essential role of local interactions
between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure
Cascades on clique-based graphs
peer-reviewedWe present an analytical approach to determining the expected cascade size in a broad range of dynamical models on the class of highly clustered random graphs introduced by Gleeson [J. P. Gleeson, Phys. Rev. E 80, 036107 (2009)]. A condition for the existence of global cascades is also derived. Applications of this approach include analyses of percolation, and Watts's model. We show how our techniques can be used to study the effects of in-group bias in cascades on social networks.PUBLISHEDpeer-reviewe
Attention on Weak Ties in Social and Communication Networks
Granovetter's weak tie theory of social networks is built around two central
hypotheses. The first states that strong social ties carry the large majority
of interaction events; the second maintains that weak social ties, although
less active, are often relevant for the exchange of especially important
information (e.g., about potential new jobs in Granovetter's work). While
several empirical studies have provided support for the first hypothesis, the
second has been the object of far less scrutiny. A possible reason is that it
involves notions relative to the nature and importance of the information that
are hard to quantify and measure, especially in large scale studies. Here, we
search for empirical validation of both Granovetter's hypotheses. We find clear
empirical support for the first. We also provide empirical evidence and a
quantitative interpretation for the second. We show that attention, measured as
the fraction of interactions devoted to a particular social connection, is high
on weak ties --- possibly reflecting the postulated informational purposes of
such ties --- but also on very strong ties. Data from online social media and
mobile communication reveal network-dependent mixtures of these two effects on
the basis of a platform's typical usage. Our results establish a clear
relationships between attention, importance, and strength of social links, and
could lead to improved algorithms to prioritize social media content
Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines
Researchers' networks have been subject to active modeling and analysis.
Earlier literature mostly focused on citation or co-authorship networks
reconstructed from annotated scientific publication databases, which have
several limitations. Recently, general-purpose web search engines have also
been utilized to collect information about social networks. Here we
reconstructed, using web search engines, a network representing the relatedness
of researchers to their peers as well as to various research topics.
Relatedness between researchers and research topics was characterized by
visibility boost-increase of a researcher's visibility by focusing on a
particular topic. It was observed that researchers who had high visibility
boosts by the same research topic tended to be close to each other in their
network. We calculated correlations between visibility boosts by research
topics and researchers' interdisciplinarity at individual level (diversity of
topics related to the researcher) and at social level (his/her centrality in
the researchers' network). We found that visibility boosts by certain research
topics were positively correlated with researchers' individual-level
interdisciplinarity despite their negative correlations with the general
popularity of researchers. It was also found that visibility boosts by
network-related topics had positive correlations with researchers' social-level
interdisciplinarity. Research topics' correlations with researchers'
individual- and social-level interdisciplinarities were found to be nearly
independent from each other. These findings suggest that the notion of
"interdisciplinarity" of a researcher should be understood as a
multi-dimensional concept that should be evaluated using multiple assessment
means.Comment: 20 pages, 7 figures. Accepted for publication in PLoS On
Universal fractal scaling of self-organized networks
There is an abundance of literature on complex networks describing a variety of relationships among units in social, biological, and technological systems. Such networks, consisting of interconnected nodes, are often self-organized, naturally emerging without any overarching designs on topological structure yet enabling efficient interactions among nodes. Here we show that the number of nodes and the density of connections in such self-organized networks exhibit a power law relationship. We examined the size and connection density of 46 self-organizing networks of various biological, social, and technological origins, and found that the size-density relationship follows a fractal relationship spanning over 6 orders of magnitude. This finding indicates that there is an optimal connection density in self-organized networks following fractal scaling regardless of their sizes
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