417,962 research outputs found
Evolution of Ego-networks in Social Media with Link Recommendations
Ego-networks are fundamental structures in social graphs, yet the process of
their evolution is still widely unexplored. In an online context, a key
question is how link recommender systems may skew the growth of these networks,
possibly restraining diversity. To shed light on this matter, we analyze the
complete temporal evolution of 170M ego-networks extracted from Flickr and
Tumblr, comparing links that are created spontaneously with those that have
been algorithmically recommended. We find that the evolution of ego-networks is
bursty, community-driven, and characterized by subsequent phases of explosive
diameter increase, slight shrinking, and stabilization. Recommendations favor
popular and well-connected nodes, limiting the diameter expansion. With a
matching experiment aimed at detecting causal relationships from observational
data, we find that the bias introduced by the recommendations fosters global
diversity in the process of neighbor selection. Last, with two link prediction
experiments, we show how insights from our analysis can be used to improve the
effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search
and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl
Science Models as Value-Added Services for Scholarly Information Systems
The paper introduces scholarly Information Retrieval (IR) as a further
dimension that should be considered in the science modeling debate. The IR use
case is seen as a validation model of the adequacy of science models in
representing and predicting structure and dynamics in science. Particular
conceptualizations of scholarly activity and structures in science are used as
value-added search services to improve retrieval quality: a co-word model
depicting the cognitive structure of a field (used for query expansion), the
Bradford law of information concentration, and a model of co-authorship
networks (both used for re-ranking search results). An evaluation of the
retrieval quality when science model driven services are used turned out that
the models proposed actually provide beneficial effects to retrieval quality.
From an IR perspective, the models studied are therefore verified as expressive
conceptualizations of central phenomena in science. Thus, it could be shown
that the IR perspective can significantly contribute to a better understanding
of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric
Understanding the Drivers of Urban Expansion: Case Study of Seville Province
Urban development has accelerated across the globe in recent decades. Much of this development has not been concentrated in cities, but has occurred as dispersed, low-density development outside of major centers but within their area of economic influence, along transport networks, in coastal areas, or close to areas of high natural value. This research focuses on the case study of the province of Seville, Spain, which has experienced notable urban expansion in recent years. We present a systems approach to model dependence between economic development and distribution of urban and non-urban land in this region from data collection to model validation. An extensive search of available indicators of socioeconomic development in this region has been carried out. We apply this data to create a generic statistical model of urban expansion, which links land-use patterns with population density and other indicators of economic growth. The model is tested across the whole Seville region and in its sub-regions to derive drivers of urban expansion in this territory
Applying Rule Ensembles to the Search for Super-Symmetry at the Large Hadron Collider
In this note we give an example application of a recently presented
predictive learning method called Rule Ensembles. The application we present is
the search for super-symmetric particles at the Large Hadron Collider. In
particular, we consider the problem of separating the background coming from
top quark production from the signal of super-symmetric particles. The method
is based on an expansion of base learners, each learner being a rule, i.e. a
combination of cuts in the variable space describing signal and background.
These rules are generated from an ensemble of decision trees. One of the
results of the method is a set of rules (cuts) ordered according to their
importance, which gives useful tools for diagnosis of the model. We also
compare the method to a number of other multivariate methods, in particular
Artificial Neural Networks, the likelihood method and the recently presented
boosted decision tree method. We find better performance of Rule Ensembles in
all cases. For example for a given significance the amount of data needed to
claim SUSY discovery could be reduced by 15 % using Rule Ensembles as compared
to using a likelihood method.Comment: 24 pages, 7 figures, replaced to match version accepted for
publication in JHE
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