Moghadam, Nastaran NavidRamamoorthy, RameshNazarimehr, FahimehRajagopal, Karthikeyan...
22页查看更多>>摘要：Researchers are eager to understand how real-world systems respond to environmental parameter changes, especially in complex networks. In biological systems like genetic networks or ecological systems, the presence of agents in the networks has been proved. Hence, studying the tipping points and finding a way to manage them can prevent the extinction of natural networks. In this paper, the tipping points of a network of the biomass model are studied. Two cases are discussed for the bifurcations of the network. In the first case, the parameters of the nodes are varied globally, and their bifurcations are studied in various cases of parameter changings. In the second case, the parameters are changed locally, and the tipping points' propagation through the neighbor nodes is discussed. However, the focus of the paper is on the bifurcations of the complex network model; we hope that this study can help understand the tipping points of real-world complex systems. (C)& nbsp;2021 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Serrano, Alfredo BlancoAllen-Perkins, AlfonsoSilva Andrade, Roberto Fernandes
12页查看更多>>摘要：This work resumes the investigation on discrete-time super-diffusive in Levy random walks defined on networks by using a inverse problem approach, with a focus on 2D-tori. Imposing that the mean square displacement of the walker should be proportional to t gamma, we use a Markov Chain formalism to evaluate a fine tuned time-dependent probability distribution of long-distance jumps the walker should use to meet this dependency. Despite its wide applicability, calculations are time-intensive, with a computing time proportional to the number of nodes in the graph to a power > 3.4. Here it is shown that, by using the circulant property satisfied by the adjacency matrices of a class of tori, it is possible to significantly speed up the calculations. For the purpose of comparison, the inverse super-diffusion problem is solved for two tori based on finite patches of the two-dimensional square lattice, namely the usual (non-circulant) and the helical (circulant) ones. The results of the latter, based on derived new expressions to compute the mean square displacement valid for circulant tori, are in complete agreement with those derived using general expressions, even if the computing time increases with respect to the number of nodes with a significantly smaller exponent ? 2.1. . Numerical simulations in both tori types also reproduce super-diffusion when using the time dependent probability distributions obtained for the helical one. The results suggest that this time efficient approach can be extended to model super-diffusion on cubic and hyper-cubic lattices. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Zheng, HongweiWang, JiannanWei, WeiZheng, Zhiming...
8页查看更多>>摘要：Non-periodic phenomena are common in a wide range of real-world recurrent dynamics, such as the occasional pandemic of seasonal influenza and the abrupt collapse of stock markets. In this paper we propose a biased excitable network model and illustrate the non-periodic phenomena as the collective response of a large amount of excitable individuals. In contrast with classic excitable networks, we introduce the bias of external stimuli that affects the exact behavior of each individual rather than its own inherent property. Based on the locally tree-like topology, we make a second order approximation on the network activity with diminishing stimuli. Result shows that the self-sustainment of network dynamics is determined by the largest eigenvalue lambda of the weighted adjacent matrix. For lambda > 1 the network is self-sustained even if the stimuli intensity approaches zero. For lambda < 1, the system tends to end up in quiescent state. At critical condition lambda = 1, the dynamic range of the system, which is the range of stimuli intensity that is distinguishable according to network activity, reaches maximum value. We also find that the dynamic range can be further enhanced when nodes are more inclined to inhibitory state as a result of smaller stimuli bias. These results are well supported by numerical simulations on both synthetic and real-world networks. Based on the proposed model, we manage to reproduce similar non-periodic phenomena to those in real-world recurrent dynamics, even when the intensity of external stimuli remains constant. Our research shed light on the mechanism of non-periodic phenomena in recurrent dynamics, which can be applied to the prevention of epidemic outbreaks as well as financial crisis. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Deffuant, GuillaumeRoubin, Thibaut
18页查看更多>>摘要：We consider a recent model in which agents hold opinions about each other and influence each other's opinions during random pair interactions. When the opinions are initially close, on the short term, all the opinions tend to increase over time. On the contrary, when the opinions are initially very unequal, the opinions about agents of high status increase, but the opinions about agents of low status tend to stagnate without gossip and to decrease with gossip. We derive a moment approximation of the average opinion changes that explains these observations. (C) 2021 The Author(s). Published by Elsevier B.V.
原文链接:- NSTL
- Elsevier
Huang, WeiZhang, TianyiYao, Xinwei
15页查看更多>>摘要：In this paper, we construct a novel model to study cascading failures that occur in the interdependent power-communication network. Based on this model, we employ the Q-learning algorithm to search for the optimal attack sequence against the lines in the communication network with the aim to bring the most destructive damage to the power grid. The effectiveness of the deduced optimal attack sequence is validated through numerical simulations, in which the IEEE-39 bus test system is used as the power grid and the Barabasi-Albert (BA) scale-free network is modeled as the communication network. It is found that in the interdependent power-communication network, large-scale failure in the power grid can be caused by attacking only a small fraction of lines in the communication network. In addition, under the same number of attack actions, the attack sequence resulted from Q-learning algorithm is more destructive than random attack sequence and the attack sequence based on traditional complex network characteristics. Furthermore, according to the analysis of simulation results, we identify the most vulnerable lines in the communication network and propose the corresponding protection strategy. The proposed protection strategy proves to be able to effectively reduce the number of independent optimal attack sequences that the attacker can launch and improve the robustness of the whole network. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Angulo-Brown, F.Ramirez-Moreno, M. A.Ares de Parga, G.
11页查看更多>>摘要：Using the thermodynamic restrictions imposed by Carnot theorem over an Otto cycle that operates at maximum work regime and applying them to obtain the heat capacities of electron gases stemming from different metals which provide enough electrons to behave as fermion gases with C-V = alpha T-beta T-3, the coefficients alpha and beta are calculated from a free electron gas model. These values agree with the experimental data for metals with high electronegativities; that is, only these metals with high electronegativities provide a fermion gas that is in accordance with the thermodynamic restrictions presented in this work.(C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Han, BeibeiWei, YingmeiKang, LaiWang, Qingyong...
19页查看更多>>摘要：Attributed multiplex graph clustering, which partitions graph nodes from different relationships into different clusters with each cluster retaining structure closeness and attribute homogeneity simultaneously, is a fundamental graph data analysis task. Although network embedding has shown to be useful in a variety of graph analysis tasks, research dedicated to the attributed multiplex graph clustering, an important aspect of graph network analysis, is limited. Some efforts have been made for multiplex graph networks with network embedding techniques. However, most of this work has overlooked the clustering-aware information, which has not been fully explored. To solve these issues, we propose a fast method with sequenced-based network embedding for attributed multiplex graph clustering, namely NEAMC, to efficiently capture the clustering-aware information between nodes and different relationships and place them into a uniform vector space. A novel and heuristic clustering-aware across multi relationship random walk node sampling strategy is proposed, which can not only select the relationship to proceed according to the information level of each relationship to retain the clustering-aware relationship and interaction among different relationships, but also leverage the node similarity to sample node sequences to retain the clustering aware interactive information between nodes. Therefore, NEAMC can improve the attributed multiplex graph clustering to maintain the structure closeness and attribute homogeneity of the cluster. NEAMC is evaluated on five datasets, and the results demonstrate that it outperforms existing competitive baselines. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Bhattacharyay, A.Maniar, Rohan
2页查看更多>>摘要：In this response to the comments made by A. Vezzani on our paper [Random walk model for coordinate-dependent diffusion in a force field, Physica A 584 (2021) 126348] we explain our stand in relation to the comments made. (c) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
