246 research outputs found
Fundamental structures of dynamic social networks
Social systems are in a constant state of flux with dynamics spanning from
minute-by-minute changes to patterns present on the timescale of years.
Accurate models of social dynamics are important for understanding spreading of
influence or diseases, formation of friendships, and the productivity of teams.
While there has been much progress on understanding complex networks over the
past decade, little is known about the regularities governing the
micro-dynamics of social networks. Here we explore the dynamic social network
of a densely-connected population of approximately 1000 individuals and their
interactions in the network of real-world person-to-person proximity measured
via Bluetooth, as well as their telecommunication networks, online social media
contacts, geo-location, and demographic data. These high-resolution data allow
us to observe social groups directly, rendering community detection
unnecessary. Starting from 5-minute time slices we uncover dynamic social
structures expressed on multiple timescales. On the hourly timescale, we find
that gatherings are fluid, with members coming and going, but organized via a
stable core of individuals. Each core represents a social context. Cores
exhibit a pattern of recurring meetings across weeks and months, each with
varying degrees of regularity. Taken together, these findings provide a
powerful simplification of the social network, where cores represent
fundamental structures expressed with strong temporal and spatial regularity.
Using this framework, we explore the complex interplay between social and
geospatial behavior, documenting how the formation of cores are preceded by
coordination behavior in the communication networks, and demonstrating that
social behavior can be predicted with high precision.Comment: Main Manuscript: 16 pages, 4 figures. Supplementary Information: 39
pages, 34 figure
Correlations Between Human Mobility and Social Interaction Reveal General Activity Patterns
A day in the life of a person involves a broad range of activities which are
common across many people. Going beyond diurnal cycles, a central question is:
to what extent do individuals act according to patterns shared across an entire
population? Here we investigate the interplay between different activity types,
namely communication, motion, and physical proximity by analyzing data
collected from smartphones distributed among 638 individuals. We explore two
central questions: Which underlying principles govern the formation of the
activity patterns? Are the patterns specific to each individual or shared
across the entire population? We find that statistics of the entire population
allows us to successfully predict 71\% of the activity and 85\% of the
inactivity involved in communication, mobility, and physical proximity.
Surprisingly, individual level statistics only result in marginally better
predictions, indicating that a majority of activity patterns are shared across
{our sample population}. Finally, we predict short-term activity patterns using
a generalized linear model, which suggests that a simple linear description
might be sufficient to explain a wide range of actions, whether they be of
social or of physical character
Constrained information flows in temporal networks reveal intermittent communities
Many real-world networks represent dynamic systems with interactions that
change over time, often in uncoordinated ways and at irregular intervals. For
example, university students connect in intermittent groups that repeatedly
form and dissolve based on multiple factors, including their lectures,
interests, and friends. Such dynamic systems can be represented as multilayer
networks where each layer represents a snapshot of the temporal network. In
this representation, it is crucial that the links between layers accurately
capture real dependencies between those layers. Often, however, these
dependencies are unknown. Therefore, current methods connect layers based on
simplistic assumptions that do not capture node-level layer dependencies. For
example, connecting every node to itself in other layers with the same weight
can wipe out dependencies between intermittent groups, making it difficult or
even impossible to identify them. In this paper, we present a principled
approach to estimating node-level layer dependencies based on the network
structure within each layer. We implement our node-level coupling method in the
community detection framework Infomap and demonstrate its performance compared
to current methods on synthetic and real temporal networks. We show that our
approach more effectively constrains information inside multilayer communities
so that Infomap can better recover planted groups in multilayer benchmark
networks that represent multiple modes with different groups and better
identify intermittent communities in real temporal contact networks. These
results suggest that node-level layer coupling can improve the modeling of
information spreading in temporal networks and better capture intermittent
community structure.Comment: 10 pages, 10 figures, published in PR
The Role of Gender in Social Network Organization
The digital traces we leave behind when engaging with the modern world offer
an interesting lens through which we study behavioral patterns as expression of
gender. Although gender differentiation has been observed in a number of
settings, the majority of studies focus on a single data stream in isolation.
Here we use a dataset of high resolution data collected using mobile phones, as
well as detailed questionnaires, to study gender differences in a large cohort.
We consider mobility behavior and individual personality traits among a group
of more than university students. We also investigate interactions among
them expressed via person-to-person contacts, interactions on online social
networks, and telecommunication. Thus, we are able to study the differences
between male and female behavior captured through a multitude of channels for a
single cohort. We find that while the two genders are similar in a number of
aspects, there are robust deviations that include multiple facets of social
interactions, suggesting the existence of inherent behavioral differences.
Finally, we quantify how aspects of an individual's characteristics and social
behavior reveals their gender by posing it as a classification problem. We ask:
How well can we distinguish between male and female study participants based on
behavior alone? Which behavioral features are most predictive
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