99 research outputs found
From which world is your graph?
Discovering statistical structure from links is a fundamental problem in the
analysis of social networks. Choosing a misspecified model, or equivalently, an
incorrect inference algorithm will result in an invalid analysis or even
falsely uncover patterns that are in fact artifacts of the model. This work
focuses on unifying two of the most widely used link-formation models: the
stochastic blockmodel (SBM) and the small world (or latent space) model (SWM).
Integrating techniques from kernel learning, spectral graph theory, and
nonlinear dimensionality reduction, we develop the first statistically sound
polynomial-time algorithm to discover latent patterns in sparse graphs for both
models. When the network comes from an SBM, the algorithm outputs a block
structure. When it is from an SWM, the algorithm outputs estimates of each
node's latent position.Comment: To appear in NIPS 201
Uncovering the Wider Structure of Extreme Right Communities Spanning Popular Online Networks
Recent years have seen increased interest in the online presence of extreme
right groups. Although originally composed of dedicated websites, the online
extreme right milieu now spans multiple networks, including popular social
media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any
contemporary analysis of online extreme right activity requires the
consideration of multiple data sources, rather than being restricted to a
single platform. We investigate the potential for Twitter to act as a gateway
to communities within the wider online network of the extreme right, given its
facility for the dissemination of content. A strategy for representing
heterogeneous network data with a single homogeneous network for the purpose of
community detection is presented, where these inherently dynamic communities
are tracked over time. We use this strategy to discover and analyze persistent
English and German language extreme right communities.Comment: 10 pages, 11 figures. Due to use of "sigchi" template, minor changes
were made to ensure 10 page limit was not exceeded. Minor clarifications in
Introduction, Data and Methodology section
Uncovering the wider structure of extreme right communities spanning popular online networks
AbstractRecent years have seen increased interest in the online presence of extreme right groups. Although originally composed of dedicated websites, the online extreme right milieu now spans multiple networks, including popular social media platforms such as Twitter, Facebook and YouTube. Ideally therefore, any contemporary analysis of online extreme right activity requires the consideration of multiple data sources, rather than being restricted to a single platform.We investigate the potential for Twitter to act as one possible gateway to communities within the wider online network of the extreme right, given its facility for the dissemination of content. A strategy for representing heterogeneous network data with a single homogeneous network for the purpose of community detection is presented, where these inherently dynamic communities are tracked over time. We use this strategy to discover and analyze persistent English and German language extreme right communities.Authored by Derek OâCallaghan, Derek Greene, Maura Conway, Joe Carthy and Padraig Cunningham
Block clustering of Binary Data with Gaussian Co-variables
The simultaneous grouping of rows and columns is an important technique that is increasingly used in large-scale data analysis. In this paper, we present a novel co-clustering method using co-variables in its construction. It is based on a latent block model taking into account the problem of grouping variables and clustering individuals by integrating information given by sets of co-variables. Numerical experiments on simulated data sets and an application on real genetic data highlight the interest of this approach
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