20 research outputs found
Predictability of conversation partners
Recent developments in sensing technologies have enabled us to examine the
nature of human social behavior in greater detail. By applying an information
theoretic method to the spatiotemporal data of cell-phone locations, [C. Song
et al. Science 327, 1018 (2010)] found that human mobility patterns are
remarkably predictable. Inspired by their work, we address a similar
predictability question in a different kind of human social activity:
conversation events. The predictability in the sequence of one's conversation
partners is defined as the degree to which one's next conversation partner can
be predicted given the current partner. We quantify this predictability by
using the mutual information. We examine the predictability of conversation
events for each individual using the longitudinal data of face-to-face
interactions collected from two company offices in Japan. Each subject wears a
name tag equipped with an infrared sensor node, and conversation events are
marked when signals are exchanged between sensor nodes in close proximity. We
find that the conversation events are predictable to some extent; knowing the
current partner decreases the uncertainty about the next partner by 28.4% on
average. Much of the predictability is explained by long-tailed distributions
of interevent intervals. However, a predictability also exists in the data,
apart from the contribution of their long-tailed nature. In addition, an
individual's predictability is correlated with the position in the static
social network derived from the data. Individuals confined in a community - in
the sense of an abundance of surrounding triangles - tend to have low
predictability, and those bridging different communities tend to have high
predictability.Comment: 38 pages, 19 figure
Big data analyses reveal patterns and drivers of the movements of southern elephant seals
The growing number of large databases of animal tracking provides an
opportunity for analyses of movement patterns at the scales of populations and
even species. We used analytical approaches, developed to cope with big data,
that require no a priori assumptions about the behaviour of the target agents,
to analyse a pooled tracking dataset of 272 elephant seals (Mirounga leonina)
in the Southern Ocean, that was comprised of >500,000 location estimates
collected over more than a decade. Our analyses showed that the displacements
of these seals were described by a truncated power law distribution across
several spatial and temporal scales, with a clear signature of directed
movement. This pattern was evident when analysing the aggregated tracks despite
a wide diversity of individual trajectories. We also identified marine
provinces that described the migratory and foraging habitats of these seals.
Our analysis provides evidence for the presence of intrinsic drivers of
movement, such as memory, that cannot be detected using common models of
movement behaviour. These results highlight the potential for big data
techniques to provide new insights into movement behaviour when applied to
large datasets of animal tracking.Comment: 18 pages, 5 figures, 6 supplementary figure
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
Effects of temporal correlations in social multiplex networks
Multi-layered networks represent a major advance in the description of natural complex systems, and their study has shed light on new physical phenomena. Despite its importance, however, the role of the temporal dimension in their structure and function has not been investigated in much detail so far. Here we study the temporal correlations between layers exhibited by real social multiplex networks. At a basic level, the presence of such correlations implies a certain degree of predictability in the contact pattern, as we quantify by an extension of the entropy and mutual information analyses proposed for the single-layer case. At a different level, we demonstrate that temporal correlations are a signature of a ‘multitasking’ behavior of network agents, characterized by a higher level of switching between different social activities than expected in a uncorrelated pattern. Moreover, temporal correlations significantly affect the dynamics of coupled epidemic processes unfolding on the network. Our work opens the way for the systematic study of temporal multiplex networks and we anticipate it will be of interest to researchers in a broad array of fields