18 research outputs found
Structures in supercritical scale-free percolation
Scale-free percolation is a percolation model on which can be
used to model real-world networks. We prove bounds for the graph distance in
the regime where vertices have infinite degrees. We fully characterize
transience vs. recurrence for dimension 1 and 2 and give sufficient conditions
for transience in dimension 3 and higher. Finally, we show the existence of a
hierarchical structure for parameters where vertices have degrees with infinite
variance and obtain bounds on the cluster density.Comment: Revised Definition 2.5 and an argument in Section 6, results are
unchanged. Correction of minor typos. 29 pages, 7 figure
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima
It is an ongoing debate whether and how comma selection in evolutionary
algorithms helps to escape local optima. We propose a new benchmark function to
investigate the benefits of comma selection: OneMax with randomly planted local
optima, generated by frozen noise. We show that comma selection (the
EA) is faster than plus selection (the EA) on this
benchmark, in a fixed-target scenario, and for offspring population sizes
for which both algorithms behave differently. For certain parameters,
the EA finds the target in evaluations, with
high probability (w.h.p.), while the EA) w.h.p. requires almost
evaluations.
We further show that the advantage of comma selection is not arbitrarily
large: w.h.p. comma selection outperforms plus selection at most by a factor of
for most reasonable parameter choices. We develop novel methods
for analysing frozen noise and give powerful and general fixed-target results
with tail bounds that are of independent interest.Comment: An extended abstract will be published at GECCO 202
Not all interventions are equal for the height of the second peak
In this paper we conduct a simulation study of the spread of an epidemic like
COVID-19 with temporary immunity on finite spatial and non-spatial network
models. In particular, we assume that an epidemic spreads stochastically on a
scale-free network and that each infected individual in the network gains a
temporary immunity after its infectious period is over. After the temporary
immunity period is over, the individual becomes susceptible to the virus again.
When the underlying contact network is embedded in Euclidean geometry, we model
three different intervention strategies that aim to control the spread of the
epidemic: social distancing, restrictions on travel, and restrictions on
maximal number of social contacts per node. Our first finding is that on a
finite network, a long enough average immunity period leads to extinction of
the pandemic after the first peak, analogous to the concept of "herd immunity".
For each model, there is a critical average immunity length above which
this happens. Our second finding is that all three interventions manage to
flatten the first peak (the travel restrictions most efficiently), as well as
decrease the critical immunity length , but elongate the epidemic.
However, when the average immunity length is shorter than , the price
for the flattened first peak is often a high second peak: for limiting the
maximal number of contacts, the second peak can be as high as 1/3 of the first
peak, and twice as high as it would be without intervention. Thirdly,
interventions introduce oscillations into the system and the time to reach
equilibrium is, for almost all scenarios, much longer. We conclude that
network-based epidemic models can show a variety of behaviors that are not
captured by the continuous compartmental models.Comment: 17 pages text, 27 figures of which 22 in the appendi
Distance evolutions in growing preferential attachment graphs
We study the evolution of the graph distance and weighted distance between
two fixed vertices in dynamically growing random graph models. More precisely,
we consider preferential attachment models with power-law exponent
, sample two vertices uniformly at random when the
graph has vertices, and study the evolution of the graph distance between
these two fixed vertices as the surrounding graph grows. This yields a
discrete-time stochastic process in , called the distance evolution.
We show that there is a tight strip around the function
that the
distance evolution never leaves with high probability as tends to infinity.
We extend our results to weighted distances, where every edge is equipped with
an i.i.d. copy of a non-negative random variable .Comment: 42 pages, 4 figures. Revised version with corrected typos and more
elaborate proofs. Includes correction of an error in Theorem 2.5 that
required a shift of indices in the summatio
Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining.
We study the effects of two mechanisms which increase the efficacy of contact-tracing applications (CTAs) such as the mobile phone contact-tracing applications that have been used during the COVID-19 epidemic. The first mechanism is the introduction of user referrals. We compare four scenarios for the uptake of CTAs-(1) the p% of individuals that use the CTA are chosen randomly, (2) a smaller initial set of randomly-chosen users each refer a contact to use the CTA, achieving p% in total, (3) a small initial set of randomly-chosen users each refer around half of their contacts to use the CTA, achieving p% in total, and (4) for comparison, an idealised scenario in which the p% of the population that uses the CTA is the p% with the most contacts. Using agent-based epidemiological models incorporating a geometric space, we find that, even when the uptake percentage p% is small, CTAs are an effective tool for mitigating the spread of the epidemic in all scenarios. Moreover, user referrals significantly improve efficacy. In addition, it turns out that user referrals reduce the quarantine load. The second mechanism for increasing the efficacy of CTAs is tuning the severity of quarantine measures. Our modelling shows that using CTAs with mild quarantine measures is effective in reducing the maximum hospital load and the number of people who become ill, but leads to a relatively high quarantine load, which may cause economic disruption. Fortunately, under stricter quarantine measures, the advantages are maintained but the quarantine load is reduced. Our models incorporate geometric inhomogeneous random graphs to study the effects of the presence of super-spreaders and of the absence of long-distant contacts (e.g., through travel restrictions) on our conclusions
Structures in supercritical scale-free percolation
\u3cp\u3eScale-free percolation is a percolation model on Z\u3csup\u3ed\u3c/sup\u3e which can be used to model real-world networks. We prove bounds for the graph distance in the regime where vertices have infinite degrees. We fully characterize transience versus recurrence for dimension 1 and 2 and give sufficient conditions for transience in dimension 3 and higher. Finally, we show the existence of a hierarchical structure for parameters where vertices have degrees with infinite variance and obtain bounds on the cluster density.\u3c/p\u3
Simulation data for "Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining"
This is the data that is generated by our simulations for the paper "Increasing efficacy of contact-tracing applications by user referrals and stricter quarantining". All figures are made with these data sets. This upload also contains the graphs on top of which the epidemics are simulated