100,499 research outputs found
An analytical framework to nowcast well-being using mobile phone data
An intriguing open question is whether measurements made on Big Data
recording human activities can yield us high-fidelity proxies of socio-economic
development and well-being. Can we monitor and predict the socio-economic
development of a territory just by observing the behavior of its inhabitants
through the lens of Big Data? In this paper, we design a data-driven analytical
framework that uses mobility measures and social measures extracted from mobile
phone data to estimate indicators for socio-economic development and
well-being. We discover that the diversity of mobility, defined in terms of
entropy of the individual users' trajectories, exhibits (i) significant
correlation with two different socio-economic indicators and (ii) the highest
importance in predictive models built to predict the socio-economic indicators.
Our analytical framework opens an interesting perspective to study human
behavior through the lens of Big Data by means of new statistical indicators
that quantify and possibly "nowcast" the well-being and the socio-economic
development of a territory
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Social status, defined as the relative rank or position that an individual
holds in a social hierarchy, is known to be among the most important motivating
forces in social behaviors. In this paper, we consider the notion of status
from the perspective of a position or title held by a person in an enterprise.
We study the intersection of social status and social networks in an
enterprise. We study whether enterprise communication logs can help reveal how
social interactions and individual status manifest themselves in social
networks. To that end, we use two enterprise datasets with three communication
channels --- voice call, short message, and email --- to demonstrate the
social-behavioral differences among individuals with different status. We have
several interesting findings and based on these findings we also develop a
model to predict social status. On the individual level, high-status
individuals are more likely to be spanned as structural holes by linking to
people in parts of the enterprise networks that are otherwise not well
connected to one another. On the community level, the principle of homophily,
social balance and clique theory generally indicate a "rich club" maintained by
high-status individuals, in the sense that this community is much more
connected, balanced and dense. Our model can predict social status of
individuals with 93% accuracy.Comment: 13 pages, 4 figure
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
Analysis of a large-scale weighted network of one-to-one human communication
We construct a connected network of 3.9 million nodes from mobile phone call
records, which can be regarded as a proxy for the underlying human
communication network at the societal level. We assign two weights on each edge
to reflect the strength of social interaction, which are the aggregate call
duration and the cumulative number of calls placed between the individuals over
a period of 18 weeks. We present a detailed analysis of this weighted network
by examining its degree, strength, and weight distributions, as well as its
topological assortativity and weighted assortativity, clustering and weighted
clustering, together with correlations between these quantities. We give an
account of motif intensity and coherence distributions and compare them to a
randomized reference system. We also use the concept of link overlap to measure
the number of common neighbors any two adjacent nodes have, which serves as a
useful local measure for identifying the interconnectedness of communities. We
report a positive correlation between the overlap and weight of a link, thus
providing strong quantitative evidence for the weak ties hypothesis, a central
concept in social network analysis. The percolation properties of the network
are found to depend on the type and order of removed links, and they can help
understand how the local structure of the network manifests itself at the
global level. We hope that our results will contribute to modeling weighted
large-scale social networks, and believe that the systematic approach followed
here can be adopted to study other weighted networks.Comment: 25 pages, 17 figures, 2 table
A survey on Human Mobility and its applications
Human Mobility has attracted attentions from different fields of studies such
as epidemic modeling, traffic engineering, traffic prediction and urban
planning. In this survey we review major characteristics of human mobility
studies including from trajectory-based studies to studies using graph and
network theory. In trajectory-based studies statistical measures such as jump
length distribution and radius of gyration are analyzed in order to investigate
how people move in their daily life, and if it is possible to model this
individual movements and make prediction based on them. Using graph in mobility
studies, helps to investigate the dynamic behavior of the system, such as
diffusion and flow in the network and makes it easier to estimate how much one
part of the network influences another by using metrics like centrality
measures. We aim to study population flow in transportation networks using
mobility data to derive models and patterns, and to develop new applications in
predicting phenomena such as congestion. Human Mobility studies with the new
generation of mobility data provided by cellular phone networks, arise new
challenges such as data storing, data representation, data analysis and
computation complexity. A comparative review of different data types used in
current tools and applications of Human Mobility studies leads us to new
approaches for dealing with mentioned challenges
Critical size of ego communication networks
With the help of information and communication technologies, studies on the
overall social networks have been extensively reported recently. However,
investigations on the directed Ego Communication Networks (ECNs) remain
insufficient, where an ECN stands for a sub network composed of a centralized
individual and his/her direct contacts. In this paper, the directed ECNs are
built on the Call Detail Records (CDRs), which cover more than 7 million people
of a provincial capital city in China for half a year. Results show that there
is a critical size for ECN at about 150, above which the average emotional
closeness between ego and alters drops, the balanced relationship between ego
and network collapses, and the proportion of strong ties decreases. This paper
not only demonstrate the significance of ECN size in affecting its properties,
but also shows accordance with the "Dunbar's Number". These results can be
viewed as a cross-culture supportive evidence to the well-known Social Brain
Hypothesis (SBH).Comment: 6 pages, 4 figures, 1 tabl
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