165,124 research outputs found
Theories for influencer identification in complex networks
In social and biological systems, the structural heterogeneity of interaction
networks gives rise to the emergence of a small set of influential nodes, or
influencers, in a series of dynamical processes. Although much smaller than the
entire network, these influencers were observed to be able to shape the
collective dynamics of large populations in different contexts. As such, the
successful identification of influencers should have profound implications in
various real-world spreading dynamics such as viral marketing, epidemic
outbreaks and cascading failure. In this chapter, we first summarize the
centrality-based approach in finding single influencers in complex networks,
and then discuss the more complicated problem of locating multiple influencers
from a collective point of view. Progress rooted in collective influence
theory, belief-propagation and computer science will be presented. Finally, we
present some applications of influencer identification in diverse real-world
systems, including online social platforms, scientific publication, brain
networks and socioeconomic systems.Comment: 24 pages, 6 figure
A comprehensive approach in performance evaluation for modernreal-time operating systems
In real-time computing the accurate characterization of the performance and determinism that a particular real-time operating system/hardware combination can provide for real-time applications is essential. This issue is not properly addressed by existing performance metrics mainly due to the lack of completeness and generalization. In this paper we present a set of comprehensive, easy-to-implement and useful metrics covering three basic real-time operating system features: response to external events, intertask synchronization and resource sharing, and intertask data transferring. The evaluation of real-time operating systems using a set of fine-grained metrics is fundamental to guarantee that we can reach the required determinism in real-world applications.Publicad
Safe Concurrency Introduction through Slicing
Traditional refactoring is about modifying the structure of existing code without changing its behaviour, but with the aim of making code easier to understand, modify, or reuse. In this paper, we introduce three novel refactorings for retrofitting concurrency to Erlang applications, and demonstrate how the use of program slicing makes the automation of these refactorings possible
Fast and simple decycling and dismantling of networks
Decycling and dismantling of complex networks are underlying many important
applications in network science. Recently these two closely related problems
were tackled by several heuristic algorithms, simple and considerably
sub-optimal, on the one hand, and time-consuming message-passing ones that
evaluate single-node marginal probabilities, on the other hand. In this paper
we propose a simple and extremely fast algorithm, CoreHD, which recursively
removes nodes of the highest degree from the -core of the network. CoreHD
performs much better than all existing simple algorithms. When applied on
real-world networks, it achieves equally good solutions as those obtained by
the state-of-art iterative message-passing algorithms at greatly reduced
computational cost, suggesting that CoreHD should be the algorithm of choice
for many practical purposes
Propagation and Decay of Injected One-Off Delays on Clusters: A Case Study
Analytic, first-principles performance modeling of distributed-memory
applications is difficult due to a wide spectrum of random disturbances caused
by the application and the system. These disturbances (commonly called "noise")
destroy the assumptions of regularity that one usually employs when
constructing simple analytic models. Despite numerous efforts to quantify,
categorize, and reduce such effects, a comprehensive quantitative understanding
of their performance impact is not available, especially for long delays that
have global consequences for the parallel application. In this work, we
investigate various traces collected from synthetic benchmarks that mimic real
applications on simulated and real message-passing systems in order to pinpoint
the mechanisms behind delay propagation. We analyze the dependence of the
propagation speed of idle waves emanating from injected delays with respect to
the execution and communication properties of the application, study how such
delays decay under increased noise levels, and how they interact with each
other. We also show how fine-grained noise can make a system immune against the
adverse effects of propagating idle waves. Our results contribute to a better
understanding of the collective phenomena that manifest themselves in
distributed-memory parallel applications.Comment: 10 pages, 9 figures; title change
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