3,160 research outputs found
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
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
Online Privacy as a Collective Phenomenon
The problem of online privacy is often reduced to individual decisions to
hide or reveal personal information in online social networks (OSNs). However,
with the increasing use of OSNs, it becomes more important to understand the
role of the social network in disclosing personal information that a user has
not revealed voluntarily: How much of our private information do our friends
disclose about us, and how much of our privacy is lost simply because of online
social interaction? Without strong technical effort, an OSN may be able to
exploit the assortativity of human private features, this way constructing
shadow profiles with information that users chose not to share. Furthermore,
because many users share their phone and email contact lists, this allows an
OSN to create full shadow profiles for people who do not even have an account
for this OSN.
We empirically test the feasibility of constructing shadow profiles of sexual
orientation for users and non-users, using data from more than 3 Million
accounts of a single OSN. We quantify a lower bound for the predictive power
derived from the social network of a user, to demonstrate how the
predictability of sexual orientation increases with the size of this network
and the tendency to share personal information. This allows us to define a
privacy leak factor that links individual privacy loss with the decision of
other individuals to disclose information. Our statistical analysis reveals
that some individuals are at a higher risk of privacy loss, as prediction
accuracy increases for users with a larger and more homogeneous first- and
second-order neighborhood of their social network. While we do not provide
evidence that shadow profiles exist at all, our results show that disclosing of
private information is not restricted to an individual choice, but becomes a
collective decision that has implications for policy and privacy regulation
Evolutionary Centrality and Maximal Cliques in Mobile Social Networks
This paper introduces an evolutionary approach to enhance the process of
finding central nodes in mobile networks. This can provide essential
information and important applications in mobile and social networks. This
evolutionary approach considers the dynamics of the network and takes into
consideration the central nodes from previous time slots. We also study the
applicability of maximal cliques algorithms in mobile social networks and how
it can be used to find the central nodes based on the discovered maximal
cliques. The experimental results are promising and show a significant
enhancement in finding the central nodes
Inferring Person-to-person Proximity Using WiFi Signals
Today's societies are enveloped in an ever-growing telecommunication
infrastructure. This infrastructure offers important opportunities for sensing
and recording a multitude of human behaviors. Human mobility patterns are a
prominent example of such a behavior which has been studied based on cell phone
towers, Bluetooth beacons, and WiFi networks as proxies for location. However,
while mobility is an important aspect of human behavior, understanding complex
social systems requires studying not only the movement of individuals, but also
their interactions. Sensing social interactions on a large scale is a technical
challenge and many commonly used approaches---including RFID badges or
Bluetooth scanning---offer only limited scalability. Here we show that it is
possible, in a scalable and robust way, to accurately infer person-to-person
physical proximity from the lists of WiFi access points measured by smartphones
carried by the two individuals. Based on a longitudinal dataset of
approximately 800 participants with ground-truth interactions collected over a
year, we show that our model performs better than the current state-of-the-art.
Our results demonstrate the value of WiFi signals in social sensing as well as
potential threats to privacy that they imply
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