81 research outputs found
Mean-field theory for scale-free random networks
Random networks with complex topology are common in Nature, describing
systems as diverse as the world wide web or social and business networks.
Recently, it has been demonstrated that most large networks for which
topological information is available display scale-free features. Here we study
the scaling properties of the recently introduced scale-free model, that can
account for the observed power-law distribution of the connectivities. We
develop a mean-field method to predict the growth dynamics of the individual
vertices, and use this to calculate analytically the connectivity distribution
and the scaling exponents. The mean-field method can be used to address the
properties of two variants of the scale-free model, that do not display
power-law scaling.Comment: 19 pages, 6 figure
Non-Coding RNAs Improve the Predictive Power of Network Medicine
Network Medicine has improved the mechanistic understanding of disease,
offering quantitative insights into disease mechanisms, comorbidities, and
novel diagnostic tools and therapeutic treatments. Yet, most network-based
approaches rely on a comprehensive map of protein-protein interactions,
ignoring interactions mediated by non-coding RNAs (ncRNAs). Here, we
systematically combine experimentally confirmed binding interactions mediated
by ncRNA with protein-protein interactions, constructing the first
comprehensive network of all physical interactions in the human cell. We find
that the inclusion of ncRNA, expands the number of genes in the interactome by
46% and the number of interactions by 107%, significantly enhancing our ability
to identify disease modules. Indeed, we find that 132 diseases, lacked a
statistically significant disease module in the protein-based interactome, but
have a statistically significant disease module after inclusion of
ncRNA-mediated interactions, making these diseases accessible to the tools of
network medicine. We show that the inclusion of ncRNAs helps unveil
disease-disease relationships that were not detectable before and expands our
ability to predict comorbidity patterns between diseases. Taken together, we
find that including non-coding interactions improves both the breath and the
predictive accuracy of network medicine.Comment: Paper and S
Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
An ability to predict the popularity dynamics of individual items within a
complex evolving system has important implications in an array of areas. Here
we propose a generative probabilistic framework using a reinforced Poisson
process to model explicitly the process through which individual items gain
their popularity. This model distinguishes itself from existing models via its
capability of modeling the arrival process of popularity and its remarkable
power at predicting the popularity of individual items. It possesses the
flexibility of applying Bayesian treatment to further improve the predictive
power using a conjugate prior. Extensive experiments on a longitudinal citation
dataset demonstrate that this model consistently outperforms existing
popularity prediction methods.Comment: 8 pages, 5 figure; 3 table
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