73 research outputs found
Control of Multilayer Networks
The controllability of a network is a theoretical problem of relevance in a
variety of contexts ranging from financial markets to the brain. Until now,
network controllability has been characterized only on isolated networks, while
the vast majority of complex systems are formed by multilayer networks. Here we
build a theoretical framework for the linear controllability of multilayer
networks by mapping the problem into a combinatorial matching problem. We found
that correlating the external signals in the different layers can significantly
reduce the multiplex network robustness to node removal, as it can be seen in
conjunction with a hybrid phase transition occurring in interacting Poisson
networks. Moreover we observe that multilayer networks can stabilize the fully
controllable multiplex network configuration that can be stable also when the
full controllability of the single network is not stable
Network measures for protein folding state discrimination
Proteins fold using a two-state or multi-state kinetic mechanisms, but up to now there is not a first-principle model to explain this different behavior. We exploit the network properties of protein structures by introducing novel observables to address the problem of classifying the different types of folding kinetics. These observables display a plain physical meaning, in terms of vibrational modes, possible configurations compatible with the native protein structure, and folding cooperativity. The relevance of these observables is supported by a classification performance up to 90%, even with simple classifiers such as discriminant analysis
Statistical mechanics formalism and methods for the analysis of real networks
This thesis provides a thoroughly theoretical background in network theory and shows novel applications to real problems and data. In the first chapter a general introduction to network ensembles is given, and the relations with “standard” equilibrium statistical mechanics are described. Moreover, an entropy measure is considered to analyze statistical properties of the integrated PPI-signalling-mRNA expression networks in different cases. In the second chapter multilayer networks are introduced to evaluate and quantify the correlations between real interdependent networks. Multiplex networks describing citation-collaboration interactions and patterns in colorectal cancer are presented. The last chapter is completely dedicated to control theory and its relation with
network theory. We characterise how the structural controllability of a network is affected by the fraction of low in-degree and low out-degree nodes. Finally, we present
a novel approach to the controllability of multiplex network
Emergent Complex Network Geometry
Networks are mathematical structures that are universally used to describe a
large variety of complex systems such as the brain or the Internet.
Characterizing the geometrical properties of these networks has become
increasingly relevant for routing problems, inference and data mining. In real
growing networks, topological, structural and geometrical properties emerge
spontaneously from their dynamical rules. Nevertheless we still miss a model in
which networks develop an emergent complex geometry. Here we show that a single
two parameter network model, the growing geometrical network, can generate
complex network geometries with non-trivial distribution of curvatures,
combining exponential growth and small-world properties with finite spectral
dimensionality. In one limit, the non-equilibrium dynamical rules of these
networks can generate scale-free networks with clustering and communities, in
another limit planar random geometries with non-trivial modularity. Finally we
find that these properties of the geometrical growing networks are present in a
large set of real networks describing biological, social and technological
systems.Comment: (24 pages, 7 figures, 1 table
Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Prediction
Link prediction is a crucial task in graph machine learning with diverse
applications. We explore the interplay between node attributes and graph
topology and demonstrate that incorporating pre-trained node attributes
improves the generalization power of link prediction models. Our proposed
method, UPNA (Unsupervised Pre-training of Node Attributes), solves the
inductive link prediction problem by learning a function that takes a pair of
node attributes and predicts the probability of an edge, as opposed to Graph
Neural Networks (GNN), which can be prone to topological shortcuts in graphs
with power-law degree distribution. In this manner, UPNA learns a significant
part of the latent graph generation mechanism since the learned function can be
used to add incoming nodes to a growing graph. By leveraging pre-trained node
attributes, we overcome observational bias and make meaningful predictions
about unobserved nodes, surpassing state-of-the-art performance (3X to 34X
improvement on benchmark datasets). UPNA can be applied to various pairwise
learning tasks and integrated with existing link prediction models to enhance
their generalizability and bolster graph generative models.Comment: 17 pages, 6 figure
Diabetes and acute bacterial skin and skin structure infections.
Acute bacterial skin and skin structures infections (ABSSSIs) are associated with high morbidity, costs and mortality in patients with diabetes mellitus. Their appropriate management should include several figures and a well-organized approach. This review aims to highlight the interplay between diabetes and ABSSSIs and bring out the unmet clinical needs in this area. Pathogenetic mechanisms underlying the increased risk of ABSSSIs in diabetes mellitus are multifactorial: high glucose levels play a crucial pathogenetic role in the tissue damage and delayed clinical cure. Moreover, the presence of diabetes complications (neuropathy, vasculopathy) further complicates the management of ABSSSIs in patients with diabetes. Multidrug resistance organisms should be considered in this population based on patient risk factors and local epidemiology and etiological diagnosis should be obtained whenever possible. Moreover, drug-drug interactions and drug-related adverse events (such as nephrotoxicity) should be considered in the choice of antibiotic therapy. Reducing unnecessary hospitalizations and prolonged length of hospital stay is of primary importance now, more than ever. To achieve these objectives, a better knowledge of the interplay between acute and chronic hyperglycemia, multidrug resistant etiology, and short and long-term outcome is needed. Of importance, a multidisciplinary approach is crucial to achieve full recovery of these patients
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