6,462 research outputs found
Identifying overlapping terrorist cells from the Noordin Top actor-event network
Actor-event data are common in sociological settings, whereby one registers
the pattern of attendance of a group of social actors to a number of events. We
focus on 79 members of the Noordin Top terrorist network, who were monitored
attending 45 events. The attendance or non-attendance of the terrorist to
events defines the social fabric, such as group coherence and social
communities. The aim of the analysis of such data is to learn about the
affiliation structure. Actor-event data is often transformed to actor-actor
data in order to be further analysed by network models, such as stochastic
block models. This transformation and such analyses lead to a natural loss of
information, particularly when one is interested in identifying, possibly
overlapping, subgroups or communities of actors on the basis of their
attendances to events. In this paper we propose an actor-event model for
overlapping communities of terrorists, which simplifies interpretation of the
network. We propose a mixture model with overlapping clusters for the analysis
of the binary actor-event network data, called {\tt manet}, and develop a
Bayesian procedure for inference. After a simulation study, we show how this
analysis of the terrorist network has clear interpretative advantages over the
more traditional approaches of affiliation network analysis.Comment: 24 pages, 5 figures; related R package (manet) available on CRA
Modeling heterogeneity in random graphs through latent space models: a selective review
We present a selective review on probabilistic modeling of heterogeneity in
random graphs. We focus on latent space models and more particularly on
stochastic block models and their extensions that have undergone major
developments in the last five years
Stochastic Blockmodeling for Online Advertising
Online advertising is an important and huge industry. Having knowledge of the
website attributes can contribute greatly to business strategies for
ad-targeting, content display, inventory purchase or revenue prediction.
Classical inferences on users and sites impose challenge, because the data is
voluminous, sparse, high-dimensional and noisy. In this paper, we introduce a
stochastic blockmodeling for the website relations induced by the event of
online user visitation. We propose two clustering algorithms to discover the
instrinsic structures of websites, and compare the performance with a
goodness-of-fit method and a deterministic graph partitioning method. We
demonstrate the effectiveness of our algorithms on both simulation and AOL
website dataset
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