4,779 research outputs found
Activity clocks: spreading dynamics on temporal networks of human contact
Dynamical processes on time-varying complex networks are key to understanding
and modeling a broad variety of processes in socio-technical systems. Here we
focus on empirical temporal networks of human proximity and we aim at
understanding the factors that, in simulation, shape the arrival time
distribution of simple spreading processes. Abandoning the notion of wall-clock
time in favour of node-specific clocks based on activity exposes robust
statistical patterns in the arrival times across different social contexts.
Using randomization strategies and generative models constrained by data, we
show that these patterns can be understood in terms of heterogeneous
inter-event time distributions coupled with heterogeneous numbers of events per
edge. We also show, both empirically and by using a synthetic dataset, that
significant deviations from the above behavior can be caused by the presence of
edge classes with strong activity correlations
Multiscale Analysis of Spreading in a Large Communication Network
In temporal networks, both the topology of the underlying network and the
timings of interaction events can be crucial in determining how some dynamic
process mediated by the network unfolds. We have explored the limiting case of
the speed of spreading in the SI model, set up such that an event between an
infectious and susceptible individual always transmits the infection. The speed
of this process sets an upper bound for the speed of any dynamic process that
is mediated through the interaction events of the network. With the help of
temporal networks derived from large scale time-stamped data on mobile phone
calls, we extend earlier results that point out the slowing-down effects of
burstiness and temporal inhomogeneities. In such networks, links are not
permanently active, but dynamic processes are mediated by recurrent events
taking place on the links at specific points in time. We perform a multi-scale
analysis and pinpoint the importance of the timings of event sequences on
individual links, their correlations with neighboring sequences, and the
temporal pathways taken by the network-scale spreading process. This is
achieved by studying empirically and analytically different characteristic
relay times of links, relevant to the respective scales, and a set of temporal
reference models that allow for removing selected time-domain correlations one
by one
Small But Slow World: How Network Topology and Burstiness Slow Down Spreading
Communication networks show the small-world property of short paths, but the
spreading dynamics in them turns out slow. We follow the time evolution of
information propagation through communication networks by using the SI model
with empirical data on contact sequences. We introduce null models where the
sequences are randomly shuffled in different ways, enabling us to distinguish
between the contributions of different impeding effects. The slowing down of
spreading is found to be caused mostly by weight-topology correlations and the
bursty activity patterns of individuals
Threshold model of cascades in temporal networks
Threshold models try to explain the consequences of social influence like the
spread of fads and opinions. Along with models of epidemics, they constitute a
major theoretical framework of social spreading processes. In threshold models
on static networks, an individual changes her state if a certain fraction of
her neighbors has done the same. When there are strong correlations in the
temporal aspects of contact patterns, it is useful to represent the system as a
temporal network. In such a system, not only contacts but also the time of the
contacts are represented explicitly. There is a consensus that bursty temporal
patterns slow down disease spreading. However, as we will see, this is not a
universal truth for threshold models. In this work, we propose an extension of
Watts' classic threshold model to temporal networks. We do this by assuming
that an agent is influenced by contacts which lie a certain time into the past.
I.e., the individuals are affected by contacts within a time window. In
addition to thresholds as the fraction of contacts, we also investigate the
number of contacts within the time window as a basis for influence. To
elucidate the model's behavior, we run the model on real and randomized
empirical contact datasets.Comment: 7 pages, 5 figures, 2 table
Universal features of correlated bursty behaviour
Inhomogeneous temporal processes, like those appearing in human
communications, neuron spike trains, and seismic signals, consist of
high-activity bursty intervals alternating with long low-activity periods. In
recent studies such bursty behavior has been characterized by a fat-tailed
inter-event time distribution, while temporal correlations were measured by the
autocorrelation function. However, these characteristic functions are not
capable to fully characterize temporally correlated heterogenous behavior. Here
we show that the distribution of the number of events in a bursty period serves
as a good indicator of the dependencies, leading to the universal observation
of power-law distribution in a broad class of phenomena. We find that the
correlations in these quite different systems can be commonly interpreted by
memory effects and described by a simple phenomenological model, which displays
temporal behavior qualitatively similar to that in real systems
Bursty egocentric network evolution in Skype
In this study we analyze the dynamics of the contact list evolution of
millions of users of the Skype communication network. We find that egocentric
networks evolve heterogeneously in time as events of edge additions and
deletions of individuals are grouped in long bursty clusters, which are
separated by long inactive periods. We classify users by their link creation
dynamics and show that bursty peaks of contact additions are likely to appear
shortly after user account creation. We also study possible relations between
bursty contact addition activity and other user-initiated actions like free and
paid service adoption events. We show that bursts of contact additions are
associated with increases in activity and adoption - an observation that can
inform the design of targeted marketing tactics.Comment: 7 pages, 6 figures. Social Network Analysis and Mining (2013
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