234 research outputs found
Dynamics of Vacillating Voters
We introduce the vacillating voter model in which each voter consults two
neighbors to decide its state, and changes opinion if it disagrees with either
neighbor. This irresolution leads to a global bias toward zero magnetization.
In spatial dimension d>1, anti-coarsening arises in which the linear dimension
L of minority domains grows as t^{1/(d+1)}. One consequence is that the time to
reach consensus scales exponentially with the number of voters.Comment: 4 pages, 6 figures, 2-column revtex4 forma
Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections
According to the Eurobarometer report about EU media use of May 2018, the number of European citizens who consult on-line social networks for accessing information is considerably increasing. In this work we analyse approximately 106 tweets exchanged during the last Italian elections held on March 4, 2018. Using an entropy-based null model discounting the activity of the users, we first identify potential political alliances within the group of verified accounts: if two verified users are retweeted more than expected by the non-verified ones, they are likely to be related. Then, we derive the usersâ affiliation to a coalition measuring the polarisation of unverified accounts. Finally, we study the bipartite directed representation of the tweets and retweets network, in which tweets and users are collected on the two layers. Users with the highest out-degree identify the most popular ones, whereas highest out-degree posts are the most âviralâ. We identify significant content spreaders with a procedure that allows to statistically validate the connections that cannot be explained by usersâ tweeting activity and postsâ virality, using an entropy-based null model as benchmark. The analysis of the directed network of validated retweets reveals signals of the alliances formed after the elections, highlighting commonalities of interests before the event of the national elections
The human element: Birth and development of workmen's compensation in Great Britain, 1880-1906
Thesis (B.A.) in History--University of Illinois at Urbana-Champaign, 1983.Bibliography: leaves [87-90]Microfiche of typescript. [Urbana, Ill.] : Photographic Services, University of Illinois, U of I Library, [1983]. 3 microfiches (96 frames) : negative ; 11 x 15 cm
Cross-Relation Characterization of Knowledge Networks
Knowledge networks have become increasingly important as a changing
repository of data which can be represented, studied and modeled by using
complex networks concepts and methodologies. Here we report a study of
knowledge networks corresponding to the areas of Physics and Theology, obtained
from the Wikipedia and taken at two different dates separated by 4 years. The
respective two versions of these networks were characterized in terms of their
respective cross-relation signatures, being summarized in terms of modification
indices obtained for each of the nodes that are preserved among the two
versions. The proposed methodology is first evaluated on Erdos-Renyi (ER) and
Barabasi-Albert model (BA) networks, before being tested on the knowledge
networks obtained from the Wikipedia respectively to the areas of Physics and
Theology. In the former study, it has been observed that the nodes at the core
and periphery of both types of theoretical models yielded similar modification
indices within these two groups of nodes, but with distinct values when taken
across these two groups. The study of the Physics and Theology networks
indicated that these two networks have signatures respectively similar to those
of the BA and ER models, as well as that higher modification values being
obtained for the periphery nodes, as compared to the respective core nodes.Comment: 23 pages, 15 figure
RankMerging:a supervised learning-to-rank framework to predict links in large social networks
Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging, which aims at combining information provided by various unsupervised rankings. We illustrate our method on three different kinds of social networks and show that it substantially improves the performances of unsupervised methods of ranking as well as standard supervised combination strategies. We also describe various properties of RankMerging, such as its computational complexity, its robustness to feature selection and parameter estimation and discuss its area of relevance: the prediction of an adjustable number of links on large networks
Mean-field analysis of the majority-vote model broken-ergodicity steady state
We study analytically a variant of the one-dimensional majority-vote model in
which the individual retains its opinion in case there is a tie among the
neighbors' opinions. The individuals are fixed in the sites of a ring of size
and can interact with their nearest neighbors only. The interesting feature
of this model is that it exhibits an infinity of spatially heterogeneous
absorbing configurations for whose statistical properties we
probe analytically using a mean-field framework based on the decomposition of
the -site joint probability distribution into the -contiguous-site joint
distributions, the so-called -site approximation. To describe the
broken-ergodicity steady state of the model we solve analytically the
mean-field dynamic equations for arbitrary time in the cases n=3 and 4. The
asymptotic limit reveals the mapping between the statistical
properties of the random initial configurations and those of the final
absorbing configurations. For the pair approximation () we derive that
mapping using a trick that avoids solving the full dynamics. Most remarkably,
we find that the predictions of the 4-site approximation reduce to those of the
3-site in the case of expectations involving three contiguous sites. In
addition, those expectations fit the Monte Carlo data perfectly and so we
conjecture that they are in fact the exact expectations for the one-dimensional
majority-vote model
Flow graphs: interweaving dynamics and structure
The behavior of complex systems is determined not only by the topological
organization of their interconnections but also by the dynamical processes
taking place among their constituents. A faithful modeling of the dynamics is
essential because different dynamical processes may be affected very
differently by network topology. A full characterization of such systems thus
requires a formalization that encompasses both aspects simultaneously, rather
than relying only on the topological adjacency matrix. To achieve this, we
introduce the concept of flow graphs, namely weighted networks where dynamical
flows are embedded into the link weights. Flow graphs provide an integrated
representation of the structure and dynamics of the system, which can then be
analyzed with standard tools from network theory. Conversely, a structural
network feature of our choice can also be used as the basis for the
construction of a flow graph that will then encompass a dynamics biased by such
a feature. We illustrate the ideas by focusing on the mathematical properties
of generic linear processes on complex networks that can be represented as
biased random walks and also explore their dual consensus dynamics.Comment: 4 pages, 1 figur
Local variation of hashtag spike trains and popularity in Twitter
We draw a parallel between hashtag time series and neuron spike trains. In
each case, the process presents complex dynamic patterns including temporal
correlations, burstiness, and all other types of nonstationarity. We propose
the adoption of the so-called local variation in order to uncover salient
dynamics, while properly detrending for the time-dependent features of a
signal. The methodology is tested on both real and randomized hashtag spike
trains, and identifies that popular hashtags present regular and so less bursty
behavior, suggesting its potential use for predicting online popularity in
social media.Comment: 7 pages, 7 figure
Transition from small to large world in growing networks
We examine the global organization of growing networks in which a new vertex
is attached to already existing ones with a probability depending on their age.
We find that the network is infinite- or finite-dimensional depending on
whether the attachment probability decays slower or faster than .
The network becomes one-dimensional when the attachment probability decays
faster than . We describe structural characteristics of these
phases and transitions between them.Comment: 5 page
Random Walks on Stochastic Temporal Networks
In the study of dynamical processes on networks, there has been intense focus
on network structure -- i.e., the arrangement of edges and their associated
weights -- but the effects of the temporal patterns of edges remains poorly
understood. In this chapter, we develop a mathematical framework for random
walks on temporal networks using an approach that provides a compromise between
abstract but unrealistic models and data-driven but non-mathematical
approaches. To do this, we introduce a stochastic model for temporal networks
in which we summarize the temporal and structural organization of a system
using a matrix of waiting-time distributions. We show that random walks on
stochastic temporal networks can be described exactly by an
integro-differential master equation and derive an analytical expression for
its asymptotic steady state. We also discuss how our work might be useful to
help build centrality measures for temporal networks.Comment: Chapter in Temporal Networks (Petter Holme and Jari Saramaki
editors). Springer. Berlin, Heidelberg 2013. The book chapter contains minor
corrections and modifications. This chapter is based on arXiv:1112.3324,
which contains additional calculations and numerical simulation
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