43,897 research outputs found
Predicting human preferences using the block structure of complex social networks
With ever-increasing available data, predicting individuals' preferences and
helping them locate the most relevant information has become a pressing need.
Understanding and predicting preferences is also important from a fundamental
point of view, as part of what has been called a "new" computational social
science. Here, we propose a novel approach based on stochastic block models,
which have been developed by sociologists as plausible models of complex
networks of social interactions. Our model is in the spirit of predicting
individuals' preferences based on the preferences of others but, rather than
fitting a particular model, we rely on a Bayesian approach that samples over
the ensemble of all possible models. We show that our approach is considerably
more accurate than leading recommender algorithms, with major relative
improvements between 38% and 99% over industry-level algorithms. Besides, our
approach sheds light on decision-making processes by identifying groups of
individuals that have consistently similar preferences, and enabling the
analysis of the characteristics of those groups
A network inference method for large-scale unsupervised identification of novel drug-drug interactions
Characterizing interactions between drugs is important to avoid potentially
harmful combinations, to reduce off-target effects of treatments and to fight
antibiotic resistant pathogens, among others. Here we present a network
inference algorithm to predict uncharacterized drug-drug interactions. Our
algorithm takes, as its only input, sets of previously reported interactions,
and does not require any pharmacological or biochemical information about the
drugs, their targets or their mechanisms of action. Because the models we use
are abstract, our approach can deal with adverse interactions,
synergistic/antagonistic/suppressing interactions, or any other type of drug
interaction. We show that our method is able to accurately predict
interactions, both in exhaustive pairwise interaction data between small sets
of drugs, and in large-scale databases. We also demonstrate that our algorithm
can be used efficiently to discover interactions of new drugs as part of the
drug discovery process
Understanding Complex Systems: From Networks to Optimal Higher-Order Models
To better understand the structure and function of complex systems,
researchers often represent direct interactions between components in complex
systems with networks, assuming that indirect influence between distant
components can be modelled by paths. Such network models assume that actual
paths are memoryless. That is, the way a path continues as it passes through a
node does not depend on where it came from. Recent studies of data on actual
paths in complex systems question this assumption and instead indicate that
memory in paths does have considerable impact on central methods in network
science. A growing research community working with so-called higher-order
network models addresses this issue, seeking to take advantage of information
that conventional network representations disregard. Here we summarise the
progress in this area and outline remaining challenges calling for more
research.Comment: 8 pages, 4 figure
Network-based models for social recommender systems
With the overwhelming online products available in recent years, there is an
increasing need to filter and deliver relevant personalized advice for users.
Recommender systems solve this problem by modeling and predicting individual
preferences for a great variety of items such as movies, books or research
articles. In this chapter, we explore rigorous network-based models that
outperform leading approaches for recommendation. The network models we
consider are based on the explicit assumption that there are groups of
individuals and of items, and that the preferences of an individual for an item
are determined only by their group memberships. The accurate prediction of
individual user preferences over items can be accomplished by different
methodologies, such as Monte Carlo sampling or Expectation-Maximization
methods, the latter resulting in a scalable algorithm which is suitable for
large datasets
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