72,191 research outputs found
The entropic origin of disassortativity in complex networks
Why are most empirical networks, with the prominent exception of social ones,
generically degree-degree anticorrelated, i.e. disassortative? With a view to
answering this long-standing question, we define a general class of
degree-degree correlated networks and obtain the associated Shannon entropy as
a function of parameters. It turns out that the maximum entropy does not
typically correspond to uncorrelated networks, but to either assortative
(correlated) or disassortative (anticorrelated) ones. More specifically, for
highly heterogeneous (scale-free) networks, the maximum entropy principle
usually leads to disassortativity, providing a parsimonious explanation to the
question above. Furthermore, by comparing the correlations measured in some
real-world networks with those yielding maximum entropy for the same degree
sequence, we find a remarkable agreement in various cases. Our approach
provides a neutral model from which, in the absence of further knowledge
regarding network evolution, one can obtain the expected value of correlations.
In cases in which empirical observations deviate from the neutral predictions
-- as happens in social networks -- one can then infer that there are specific
correlating mechanisms at work.Comment: 4 pages, 4 figures. Accepted in Phys. Rev. Lett. (2010
Entropy Rate of Diffusion Processes on Complex Networks
The concept of entropy rate for a dynamical process on a graph is introduced.
We study diffusion processes where the node degrees are used as a local
information by the random walkers. We describe analitically and numerically how
the degree heterogeneity and correlations affect the diffusion entropy rate. In
addition, the entropy rate is used to characterize complex networks from the
real world. Our results point out how to design optimal diffusion processes
that maximize the entropy for a given network structure, providing a new
theoretical tool with applications to social, technological and communication
networks.Comment: 4 pages (APS format), 3 figures, 1 tabl
On the Perturbation of Self-Organized Urban Street Networks
We investigate urban street networks as a whole within the frameworks of
information physics and statistical physics. Urban street networks are
envisaged as evolving social systems subject to a Boltzmann-mesoscopic entropy
conservation. For self-organized urban street networks, our paradigm has
already allowed us to recover the effectively observed scale-free distribution
of roads and to foresee the distribution of junctions. The entropy conservation
is interpreted as the conservation of the surprisal of the city-dwellers for
their urban street network. In view to extend our investigations to other urban
street networks, we consider to perturb our model for self-organized urban
street networks by adding an external surprisal drift. We obtain the statistics
for slightly drifted self-organized urban street networks. Besides being
practical and manageable, this statistics separates the macroscopic evolution
scale parameter from the mesoscopic social parameters. This opens the door to
observational investigations on the universality of the evolution scale
parameter. Ultimately, we argue that the strength of the external surprisal
drift might be an indicator for the disengagement of the city-dwellers for
their city.Comment: 22 pages, 4 figures + 1 table, LaTeX2e+BMCArt+AmSLaTeX+enote
Cluster size entropy in the Axelrod model of social influence: small-world networks and mass media
We study the Axelrod's cultural adaptation model using the concept of cluster
size entropy, that gives information on the variability of the cultural
cluster size present in the system. Using networks of different topologies,
from regular to random, we find that the critical point of the well-known
nonequilibrium monocultural-multicultural (order-disorder) transition of the
Axelrod model is unambiguously given by the maximum of the
distributions. The width of the cluster entropy distributions can be used to
qualitatively determine whether the transition is first- or second-order. By
scaling the cluster entropy distributions we were able to obtain a relationship
between the critical cultural trait and the number of cultural
features in regular networks. We also analyze the effect of the mass media
(external field) on social systems within the Axelrod model in a square
network. We find a new partially ordered phase whose largest cultural cluster
is not aligned with the external field, in contrast with a recent suggestion
that this type of phase cannot be formed in regular networks. We draw a new
phase diagram for the Axelrod model in regular networks.Comment: 21 pages, 7 figure
Entropy of dynamical social networks
Human dynamical social networks encode information and are highly adaptive.
To characterize the information encoded in the fast dynamics of social
interactions, here we introduce the entropy of dynamical social networks. By
analysing a large dataset of phone-call interactions we show evidence that the
dynamical social network has an entropy that depends on the time of the day in
a typical week-day. Moreover we show evidence for adaptability of human social
behavior showing data on duration of phone-call interactions that significantly
deviates from the statistics of duration of face-to-face interactions. This
adaptability of behavior corresponds to a different information content of the
dynamics of social human interactions. We quantify this information by the use
of the entropy of dynamical networks on realistic models of social
interactions
Location Prediction: Communities Speak Louder than Friends
Humans are social animals, they interact with different communities of
friends to conduct different activities. The literature shows that human
mobility is constrained by their social relations. In this paper, we
investigate the social impact of a person's communities on his mobility,
instead of all friends from his online social networks. This study can be
particularly useful, as certain social behaviors are influenced by specific
communities but not all friends. To achieve our goal, we first develop a
measure to characterize a person's social diversity, which we term `community
entropy'. Through analysis of two real-life datasets, we demonstrate that a
person's mobility is influenced only by a small fraction of his communities and
the influence depends on the social contexts of the communities. We then
exploit machine learning techniques to predict users' future movement based on
their communities' information. Extensive experiments demonstrate the
prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201
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