63 research outputs found
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
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
Community analysis in social networks
We present an empirical study of different social networks obtained from
digital repositories. Our analysis reveals the community structure and provides
a useful visualising technique. We investigate the scaling properties of the
community size distribution, and that find all the networks exhibit power law
scaling in the community size distributions with exponent either -0.5 or -1.
Finally we find that the networks' community structure is topologically
self-similar using the Horton-Strahler index.Comment: Submitted to European Physics Journal
Multilayer stochastic block models reveal the multilayer structure of complex networks
In complex systems, the network of interactions we observe between system's
components is the aggregate of the interactions that occur through different
mechanisms or layers. Recent studies reveal that the existence of multiple
interaction layers can have a dramatic impact in the dynamical processes
occurring on these systems. However, these studies assume that the interactions
between systems components in each one of the layers are known, while typically
for real-world systems we do not have that information. Here, we address the
issue of uncovering the different interaction layers from aggregate data by
introducing multilayer stochastic block models (SBMs), a generalization of
single-layer SBMs that considers different mechanisms of layer aggregation.
First, we find the complete probabilistic solution to the problem of finding
the optimal multilayer SBM for a given aggregate observed network. Because this
solution is computationally intractable, we propose an approximation that
enables us to verify that multilayer SBMs are more predictive of network
structure in real-world complex systems
Search and Congestion in Complex Networks
A model of communication that is able to cope simultaneously with the
problems of search and congestion is presented. We investigate the
communication dynamics in model networks and introduce a general framework that
enables a search of optimal structures.Comment: Proceedings of the Conference "Statistical Mechanics of Complex
Networks", Sitges, Spain, June 200
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