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Fast influencers in complex networks

Fang Zhou, Linyuan Lü, Manuel Sebastian Mariani
2019 Communications in nonlinear science & numerical simulation  
We find that local network properties can be used to uncover the fast influencers.  ...  Influential nodes in complex networks are typically defined as those nodes that maximize the asymptotic reach of a spreading process of interest.  ...  MSM acknowledges the University of Zürich for support through the URPP Social Networks.  ... 
doi:10.1016/j.cnsns.2019.01.032 fatcat:bglmirl7u5dfroblkkv5iayboa

Modeling and Simulating Constrained Protein Interaction Networks

Bianca Stöcker, Johannes Köster, Eli Zamir, Sven Rahmann
2017 Genomics and Computational Biology  
Further, we present an efficient model, enabling a fast simulation and analysis of many proteins in large networks.  ...  Further, we show how propagation of perturbation effects is influenced by the interplay of network topology and interaction dependencies and how to analyze this with our model.  ...  Further, we present an efficient model, enabling a fast simulation and analysis of many proteins in large networks.  ... 
doi:10.18547/gcb.2018.vol4.iss1.e100049 fatcat:grsil4l775djfjv7ks222wt6uu

Dimensioning of combined OBS/OCS networks

M. De Leenheer, C. Develder, J. Buysse, B. Dhoedt, P. Demeester
2008 2008 5th International Conference on Broadband Communications, Networks and Systems  
To cope with ever-increasing traffic demands in transport networks, all-optical switching is currently perceived as a potential solution to remove bottlenecks caused by optoelectronic conversions.  ...  to slow only or fast only approaches.  ...  In Section V, we will demonstrate that allowing reconfigurability of fast wavelengths has a negligable influence on the total network cost.  ... 
doi:10.1109/broadnets.2008.4769055 dblp:conf/broadnets/LeenheerDBDD08 fatcat:fkst4onxqzcstg2bc6f23xqavi

A Novel Complex Networks Clustering Algorithm Based on the Core Influence of Nodes

Chao Tong, Jianwei Niu, Bin Dai, Zhongyu Xie
2014 The Scientific World Journal  
It clusters faster and plays a positive role in revealing the real cluster structure of complex networks precisely.  ...  In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property.  ...  Betweenness centrality partially describes the core influence of nodes in complex networks.  ... 
doi:10.1155/2014/801854 pmid:24741359 pmcid:PMC3972856 fatcat:ytwsi37ulbawpibn56yo2y3hpu

The Wisdom of Networks: A General Adaptation and Learning Mechanism of Complex Systems

Peter Csermely
2017 Bioessays  
I hypothesize that re-occurring prior experience of complex systems mobilizes a fast response, whose attractor is encoded by their strongly connected network core.  ...  In contrast, responses to novel stimuli are often slow and require the weakly connected network periphery.  ...  In conclusion, recent data indicate that the adaptive response of many complex systems first mobilizes a fast, pre-set response of a well-connected network core.  ... 
doi:10.1002/bies.201700150 pmid:29168203 fatcat:7etnaex42vca3lia25tl447kqu

Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion

Sara Brin Rosenthal, Colin R. Twomey, Andrew T. Hartnett, Hai Shan Wu, Iain D. Couzin
2015 Proceedings of the National Academy of Sciences of the United States of America  
We find that individuals use simple, robust measures to assess behavioral changes in neighbors, and that the resulting networks by which behavior propagates throughout groups are complex, being weighted  ...  socially influential and most susceptible to social influence.  ...  it is too time consuming to make a more complex assessment and/or because a fast start is inherently am biguous, typically being a reflex response (26) .  ... 
doi:10.1073/pnas.1420068112 pmid:25825752 pmcid:PMC4403201 fatcat:cqpb7szkcngdlkke2qqu6ri7lu

Community Structure Division based on Immune Algorithm

Yuling Tian
2019 International Journal of Performability Engineering  
In this paper, an efficient method of community structure division in complex networks based on the immune algorithm is proposed.  ...  Community structure division makes complex networks easy to understand. However, most community structure division methods often need the number of communities and have low efficiency.  ...  Introduction Community structure division helps researchers understand the organization of complex networks and plays an important role in network analysis. Lin et al.  ... 
doi:10.23940/ijpe.19.04.p5.11031111 fatcat:5gfrmsar55at3linnqxj5572ay

A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

Johan Kerkhofs, Liesbet Geris, Manuela Helmer-Citterich
2015 PLoS ONE  
Given that this type of information is readily available, it may prove advantageous to incorporate in the gene network model, albeit at the cost of increased complexity.  ...  The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour.  ...  The same holds true for the influence of priority classes in the chondrocyte network.  ... 
doi:10.1371/journal.pone.0130033 pmid:26067297 pmcid:PMC4489432 fatcat:jgewkq24fzfcvcf4dyplt4rqaa

Coherence resonance in influencer networks

Ralf Tönjes, Carlos E. Fiore, Tiago Pereira
2021 Nature Communications  
Our results reveal that the influencer backbone can sharply increase the dynamical response in complex systems of coupled oscillators.  ...  Here, for oscillatory dynamics on influencer networks, we show that subjecting influencers to an optimal intensity of noise can result in enhanced network synchronization.  ...  This work was supported in parts by the DFG and FAPESP through the IRTG 1740/TRP 2015/50122-0, by the Center for Research in Mathematics Applied to Industry (FAPESP Cemeai grant 2013/07375-0) and grants  ... 
doi:10.1038/s41467-020-20441-4 pmid:33398017 fatcat:wt7g5raq2nc25es5ofrmimg4zu

Study on Fast-Changing Mixed-Modulation Recognition Based on Neural Network Algorithms

2020 KSII Transactions on Internet and Information Systems  
As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.  ...  Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%.  ...  The influences of multiple parameters, namely the SNR, mixed-modulation speed, dataset length, and algorithm complexity, for fast-changing mixed-modulation recognition were also analyzed.  ... 
doi:10.3837/tiis.2020.12.003 fatcat:rs24lyfqvjdttbhhhli6ocy6dq

Microstructural evolution in an austenitic stainless steel fusion reactor first wall

R.E. Stoller, G.R. Odette
1986 Journal of Nuclear Materials  
network can be simultaneously annihilated by a climb/glide process.  ...  However, the transmutant helium production in an austenitic stainless steel first wall will exceed that in fast reactor fuel cladding by about a factor of 30.  ...  The loop evolution in turn influences the network dislocation density and the predicted swelling.  ... 
doi:10.1016/0022-3115(86)90067-x fatcat:6axwhdz2ircijpyobpc5honqia

Could Proactive Link-State Routed Wireless Networks Benefit from Local Fast Reroute?

Audun Fosselie Hansen, Geir Egeland, Paal Engelstad
2008 6th Annual Communication Networks and Services Research Conference (cnsr 2008)  
The communication performance in wireless networks is often heavily influenced by failures caused by node mobility and radio disturbance.  ...  To our best knowledge, no published work presents a similar study on fast recovery in proactive link-state routed wireless networks.  ...  This may influence the forwarding delay and throughput in the network.  ... 
doi:10.1109/cnsr.2008.26 dblp:conf/cnsr/HansenEE08 fatcat:4hwl7ldskbcdxavmo5v56hws7y

Evaluation for Sortie Generation Capacity of the Carrier Aircraft Based on the Variable Structure RBF Neural Network with the Fast Learning Rate

Tiantian Luan, Mingxiao Sun, Guoqing Xia, Daidai Chen
2018 Complexity  
The convergence rate of the RBF neural network is improved by using the robust regression algorithm and the fast learning rate algorithm.  ...  This paper proposes a new variable structure radial basis function (VS-RBF) with a fast learning rate, in order to solve the problem of structural optimization design and parameter learning algorithm for  ...  The evaluation for sortie generation capacity of the carrier aircraft is complex, due to the mutual influence and complex nonlinear of factors.  ... 
doi:10.1155/2018/6950124 fatcat:q2fjnjqhenhzjbzxeui7zy4zea

Recurrent cortical networks with realistic horizontal connectivities show complex dynamics

Nicole Voges, Laurent Perrine
2009 BMC Neuroscience  
Considered networks Figure 1 Considered networks. Red symbols indicate post-synaptic projection targets of an excitatory cell, cyan symbols those of an inhibitory cell.  ...  Neuronal wiring in the cortex, however, shows a complex spatial pattern composed of local and long-range patchy connections, i.e., spatially clustered synapses [3, 4] .  ...  We analyze to what extent such geometric traits influence the dynamics of cortical network models.  ... 
doi:10.1186/1471-2202-10-s1-p304 fatcat:ymcjoj2xtfejdi52jrralursgq

Advances in nonlinear dynamics of complex networks: adaptivity, stochasticity, and delays

Vladimir Nekorkin, Vladimir Klinshov
2018 The European Physical Journal Special Topics  
The influence of noise may lead to switching between various levels of localized activity in neural networks [6] .  ...  The contributions collected here provide a broad picture of how various aspects of complexity may influence the collective behavior of networks.  ...  The intermittency in delay-coupled fast-slow systems generating extreme events was investigated [16] .  ... 
doi:10.1140/epjst/e2018-800191-9 fatcat:v3rpqsxcgfeldchoyqncvukdmu
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