30,457 research outputs found
Predicting triadic closure in networks using communicability distance functions
We propose a communication-driven mechanism for predicting triadic closure in
complex networks. It is mathematically formulated on the basis of
communicability distance functions that account for the quality of
communication between nodes in the network. We study real-world networks
and show that the proposed method predicts correctly of triadic closures
in these networks, in contrast to the predicted by a random mechanism.
We also show that the communication-driven method outperforms the random
mechanism in explaining the clustering coefficient, average path length, and
average communicability. The new method also displays some interesting features
with regards to optimizing communication in networks
Exact and approximate moment closures for non-Markovian network epidemics
Moment-closure techniques are commonly used to generate low-dimensional
deterministic models to approximate the average dynamics of stochastic systems
on networks. The quality of such closures is usually difficult to asses and the
relationship between model assumptions and closure accuracy are often
difficult, if not impossible, to quantify. Here we carefully examine some
commonly used moment closures, in particular a new one based on the concept of
maximum entropy, for approximating the spread of epidemics on networks by
reconstructing the probability distributions over triplets based on those over
pairs. We consider various models (SI, SIR, SEIR and Reed-Frost-type) under
Markovian and non-Markovian assumption characterising the latent and infectious
periods. We initially study two special networks, namely the open triplet and
closed triangle, for which we can obtain analytical results. We then explore
numerically the exactness of moment closures for a wide range of larger motifs,
thus gaining understanding of the factors that introduce errors in the
approximations, in particular the presence of a random duration of the
infectious period and the presence of overlapping triangles in a network. We
also derive a simpler and more intuitive proof than previously available
concerning the known result that pair-based moment closure is exact for the
Markovian SIR model on tree-like networks under pure initial conditions. We
also extend such a result to all infectious models, Markovian and
non-Markovian, in which susceptibles escape infection independently from each
infected neighbour and for which infectives cannot regain susceptible status,
provided the network is tree-like and initial conditions are pure. This works
represent a valuable step in deepening understanding of the assumptions behind
moment closure approximations and for putting them on a more rigorous
mathematical footing.Comment: Main text (45 pages, 11 figures and 3 tables) + supplementary
material (12 pages, 10 figures and 1 table). Accepted for publication in
Journal of Theoretical Biology on 27th April 201
Critical Cooperation Range to Improve Spatial Network Robustness
A robust worldwide air-transportation network (WAN) is one that minimizes the
number of stranded passengers under a sequence of airport closures. Building on
top of this realistic example, here we address how spatial network robustness
can profit from cooperation between local actors. We swap a series of links
within a certain distance, a cooperation range, while following typical
constraints of spatially embedded networks. We find that the network robustness
is only improved above a critical cooperation range. Such improvement can be
described in the framework of a continuum transition, where the critical
exponents depend on the spatial correlation of connected nodes. For the WAN we
show that, except for Australia, all continental networks fall into the same
universality class. Practical implications of this result are also discussed
Multiscale Computations on Neural Networks: From the Individual Neuron Interactions to the Macroscopic-Level Analysis
We show how the Equation-Free approach for multi-scale computations can be
exploited to systematically study the dynamics of neural interactions on a
random regular connected graph under a pairwise representation perspective.
Using an individual-based microscopic simulator as a black box coarse-grained
timestepper and with the aid of simulated annealing we compute the
coarse-grained equilibrium bifurcation diagram and analyze the stability of the
stationary states sidestepping the necessity of obtaining explicit closures at
the macroscopic level. We also exploit the scheme to perform a rare-events
analysis by estimating an effective Fokker-Planck describing the evolving
probability density function of the corresponding coarse-grained observables
In silico generation of novel, drug-like chemical matter using the LSTM neural network
The exploration of novel chemical spaces is one of the most important tasks
of cheminformatics when supporting the drug discovery process. Properly
designed and trained deep neural networks can provide a viable alternative to
brute-force de novo approaches or various other machine-learning techniques for
generating novel drug-like molecules. In this article we present a method to
generate molecules using a long short-term memory (LSTM) neural network and
provide an analysis of the results, including a virtual screening test. Using
the network one million drug-like molecules were generated in 2 hours. The
molecules are novel, diverse (contain numerous novel chemotypes), have good
physicochemical properties and have good synthetic accessibility, even though
these qualities were not specific constraints. Although novel, their structural
features and functional groups remain closely within the drug-like space
defined by the bioactive molecules from ChEMBL. Virtual screening using the
profile QSAR approach confirms that the potential of these novel molecules to
show bioactivity is comparable to the ChEMBL set from which they were derived.
The molecule generator written in Python used in this study is available on
request.Comment: in this version fixed some reference number
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