12,636 research outputs found
Random graph ensembles with many short loops
Networks observed in the real world often have many short loops. This
violates the tree-like assumption that underpins the majority of random graph
models and most of the methods used for their analysis. In this paper we sketch
possible research routes to be explored in order to make progress on networks
with many short loops, involving old and new random graph models and ideas for
novel mathematical methods. We do not present conclusive solutions of problems,
but aim to encourage and stimulate new activity and in what we believe to be an
important but under-exposed area of research. We discuss in more detail the
Strauss model, which can be seen as the `harmonic oscillator' of `loopy' random
graphs, and a recent exactly solvable immunological model that involves random
graphs with extensively many cliques and short loops.Comment: 18 pages, 10 figures,Mathematical Modelling of Complex Systems (Paris
2013) conferenc
Cycle-centrality in complex networks
Networks are versatile representations of the interactions between entities
in complex systems. Cycles on such networks represent feedback processes which
play a central role in system dynamics. In this work, we introduce a measure of
the importance of any individual cycle, as the fraction of the total
information flow of the network passing through the cycle. This measure is
computationally cheap, numerically well-conditioned, induces a centrality
measure on arbitrary subgraphs and reduces to the eigenvector centrality on
vertices. We demonstrate that this measure accurately reflects the impact of
events on strategic ensembles of economic sectors, notably in the US economy.
As a second example, we show that in the protein-interaction network of the
plant Arabidopsis thaliana, a model based on cycle-centrality better accounts
for pathogen activity than the state-of-art one. This translates into
pathogen-targeted-proteins being concentrated in a small number of triads with
high cycle-centrality. Algorithms for computing the centrality of cycles and
subgraphs are available for download
Controlled generation of field squeezing with cold atomic clouds coupled to a superconducting transmission line resonator
We propose an efficient method for controlled generation of field squeezing
with cold atomic clouds trapped close to a superconducting transmission line
resonator. It is shown that, based on the coherent strong magnetic coupling
between the collective atomic spins and microwave fields in the transmission
line resonator, two-mode or single mode field squeezed states can be generated
through coherent control on the dynamics of the system. The degree of squeezing
and preparing time can be directly controlled through tuning the external
classical fields. This protocol may offer a promising platform for implementing
scalable on-chip quantum information processing with continuous variables.Comment: accepted by Phys. Rev.
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
We examine a network of learners which address the same classification task
but must learn from different data sets. The learners cannot share data but
instead share their models. Models are shared only one time so as to preserve
the network load. We introduce DELCO (standing for Decentralized Ensemble
Learning with COpulas), a new approach allowing to aggregate the predictions of
the classifiers trained by each learner. The proposed method aggregates the
base classifiers using a probabilistic model relying on Gaussian copulas.
Experiments on logistic regressor ensembles demonstrate competing accuracy and
increased robustness in case of dependent classifiers. A companion python
implementation can be downloaded at https://github.com/john-klein/DELC
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