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Corrigendum to “Promotion and resignation in employee networks” [Physica A 444 (2016) 442–447]

Physica A: Statistical Mechanics and its Applications 2020年 555卷

作者： Jia Yuan Qian-Ming Zhang Jian Gao Linyan Zhang Xue-Song Wan Xiao-Jun Yu Tao Zhou Chengdu Institute of Public Administration Chengdu 610110 People’s Republic of China School of Management and Economics University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China CompleX Lab Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China Center for Polymer Studies Department of Physics Boston University Boston 02215 United States Hire Big Data (Chengdu) LTD. Chengdu 611731 People’s Republic of China Beijing Strong Union Technology Co. Ltd. Beijing 100086 People’s Republic of China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 People’s Republic of China

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Synergistic interactions promote behavior spreading and alter phase transitions on multiplex networks

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Physical Review E 2018年第2期97卷 022311-022311页

作者： Quan-Hui Liu Wei Wang Shi-Min Cai Ming Tang Ying-Cheng Lai Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University Boston Massachusetts 02115 USA College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Center for Polymer Studies and Department of Physics Boston University Boston Massachusetts 02215 USA School of Information Science Technology East China Normal University Shanghai 200241 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA

Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system.

关键词： Cascades Percolation phase transition Spreading

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Impacts of opinion leaders on social contagions

arXiv

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arXiv 2018年

作者： Liu, Quan-Hui Lü, Feng-Mao Zhang, Qian Tang, Ming Zhou, Tao Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA02115 United States Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China School of Information Science Technology East China Normal University Shanghai200241 China Institute of Fundamental and Frontier Science University of Electronic Science and Technology of China Chengdu611731 China

Opinion leaders are ubiquitous in both online and offline social networks, but the impacts of opinion leaders on social behavior contagions are still not fully understood, especially by using a mathematical model. Here we generalize the classical Watts threshold model and address the influences of the opinion leaders, where an individual adopts a new behavior if one of his/her opinion leaders adopts the behavior. First, we choose the opinion leaders randomly from all individuals in the network and find the impacts of opinion leaders make other individuals adopt the behavior more easily. Specifically, the existence of opinion leaders reduces the lowest mean degree of the network required for the global behavior adoption, and increases the highest mean degree of the network that the global behavior adoption can occur. Besides, the introduction of opinion leaders accelerates the behavior adoption, but does not change the adoption order of individuals. The developed theoretical predictions agree with the simulation results. Second, we randomly choose the opinion leaders from the top h% of the highest degree individuals, and find an optimal h% for the network with the lowest mean degree that the global behavior adoption can occur. Meanwhile, the influences of opinion leaders on accelerating the adoption of behaviors become less significant and can even be ignored when reducing the value of h%. Copyright © 2018, The Authors. All rights reserved.

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Explosive spreading on complex networks: The role of synergy

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Physical Review E 2017年第4期95卷 042320-042320页

作者： Quan-Hui Liu Wei Wang Ming Tang Tao Zhou Ying-Cheng Lai Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA

In spite of the vast literature on spreading dynamics on complex networks, the role of local synergy, i.e., the interaction of elements that when combined produce a total effect greater than the sum of the individual elements, has been studied but only for irreversible spreading dynamics. Reversible spreading dynamics are ubiquitous but their interplay with synergy has remained unknown. To fill this knowledge gap, we articulate a model to incorporate local synergistic effect into the classical susceptible-infected-susceptible process, in which the probability for a susceptible node to become infected through an infected neighbor is enhanced when the neighborhood of the latter contains a number of infected nodes. We derive master equations incorporating the synergistic effect, with predictions that agree well with the numerical results. A striking finding is that when a parameter characterizing the strength of the synergy reinforcement effect is above a critical value, the steady-state density of the infected nodes versus the basic transmission rate exhibits an explosively increasing behavior and a hysteresis loop emerges. In fact, increasing the synergy strength can promote the spreading and reduce the invasion and persistence thresholds of the hysteresis loop. A physical understanding of the synergy promoting explosive spreading and the associated hysteresis behavior can be obtained through a mean-field analysis.

关键词： Collective behavior in networks Discontinuous phase transition Spreading Networks & random structures Poisson degree distribution networks Scale free & inhomogeneous networks Bifurcation analysis Dynamical mean field theory SIS model

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Synergistic interactions promote behavior spreading and alter phase transition on multiplex Networks

arXiv

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arXiv 2018年

作者： Liu, Quan-Hui Wang, Wei Cai, Shi-Min Tang, Ming Lai, Ying-Cheng Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Laboratory for Modeling of Biological and Socio-technical Systems Northeastern University BostonMA02115 United States College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Center for Polymer Studies and Department of Physics Boston University BostonMA02215 United States School of Information Science Technology East China Normal University Shanghai200241 China School of Electrical Arizona State University Computer and Energy Engineering TempeAZ85287 United States

Synergistic interactions are ubiquitous in the real world. Recent studies have revealed that, for a single-layer network, synergy can enhance spreading and even induce an explosive contagion. There is at the present a growing interest in behavior spreading dynamics on multiplex networks. What is the role of synergistic interactions in behavior spreading in such networked systems? To address this question, we articulate a synergistic behavior spreading model on a double layer network, where the key manifestation of the synergistic interactions is that the adoption of one behavior by a node in one layer enhances its probability of adopting the behavior in the other layer. A general result is that synergistic interactions can greatly enhance the spreading of the behaviors in both layers. A remarkable phenomenon is that the interactions can alter the nature of the phase transition associated with behavior adoption or spreading dynamics. In particular, depending on the transmission rate of one behavior in a network layer, synergistic interactions can lead to a discontinuous (first-order) or a continuous (second-order) transition in the adoption scope of the other behavior with respect to its transmission rate. A surprising two-stage spreading process can arise: Due to synergy, nodes having adopted one behavior in one layer adopt the other behavior in the other layer and then prompt the remaining nodes in this layer to quickly adopt the behavior. Analytically, we develop an edge-based compartmental theory and perform a bifurcation analysis to fully understand, in the weak synergistic interaction regime where the dynamical correlation between the network layers is negligible, the role of the interactions in promoting the social behavioral spreading dynamics in the whole system. Copyright © 2018, The Authors. All rights reserved.

关键词： Network layers

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Extortion provides alternative routes to the evolution of cooperation in structured populations

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Physical Review E 2017年第5期95卷 052302-052302页

作者： Xiongrui Xu Zhihai Rong Zhi-Xi Wu Tao Zhou Chi Kong Tse CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Institute of Computational Physics and Complex Systems Lanzhou University Lanzhou Gansu 730000 People's Republic of China

In this paper, we study the evolution of cooperation in structured populations (individuals are located on either a regular lattice or a scale-free network) in the context of repeated games by involving three types of strategies, namely, unconditional cooperation, unconditional defection, and extortion. The strategy updating of the players is ruled by the replicator-like dynamics. We find that extortion strategies can act as catalysts to promote the emergence of cooperation in structured populations via different mechanisms. Specifically, on regular lattice, extortioners behave as both a shield, which can enwrap cooperators inside and keep them away from defectors, and a spear, which can defeat those surrounding defectors with the help of the neighboring cooperators. Particularly, the enhancement of cooperation displays a resonance-like behavior, suggesting the existence of optimal extortion strength mostly favoring the evolution of cooperation, which is in good agreement with the predictions from the generalized mean-field approximation theory. On scale-free network, the hubs, who are likely occupied by extortioners or defectors at the very beginning, are then prone to be conquered by cooperators on small-degree nodes as time elapses, thus establishing a bottom-up mechanism for the emergence and maintenance of cooperation.

关键词： Population dynamics Self-organized systems Social systems

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Complex network analysis of brain functional connectivity under a multi-step cognitive task

arXiv

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arXiv 2017年

作者： Cai, Shi-Min Chen, Wei Liu, Dong-Bai Tang, Ming Chen, Xun Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610073 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610073 China Department of Neurology Affiliated Jiangyin Hospital of Southeast University of Medical College Jiangyin214400 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving with consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed base on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and behaves obvious differences restricted to order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse function connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain. Copyright © 2017, The Authors. All rights reserved.

关键词： Brain

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Publisher Correction: Equivalence and its invalidation between non-Markovian and Markovian spreading dynamics on complex networks

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Nature communications 2019年第1期10卷 4677页

作者： Mi Feng Shi-Min Cai Ming Tang Ying-Cheng Lai School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai 200241 China. Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China. Big Data Research Center University of Electronic Science and Technology of China Chengdu 611731 China. School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai 200241 China. tangminghan007@***. Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China. tangminghan007@***. School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ 85287 USA.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Dynamics of social contagions with local trend imitation

arXiv

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arXiv 2017年

作者： Zhu, Xuzhen Wang, Wei Cai, Shimin Eugene Stanley, H. State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Center for Polymer Studies and Department of Physics Boston University BostonMA02215 United States

Research on social contagion dynamics has not yet including a theoretical analysis of the ubiquitous local trend imitation (LTI) characteristic. We propose a social contagion model with a tent-like adoption probability distribution to investigate the effect of this LTI characteristic on behavior spreading. We also propose a generalized edgebased compartmental theory to describe the proposed model. Through extensive numerical simulations and theoretical analyses, we find a crossover in the phase transition: when the LTI capacity is strong, the growth of the final behavior adoption size exhibits a second-order phase transition. When the LTI capacity is weak, we see a first-order phase transition. For a given behavioral information transmission probability, there is an optimal LTI capacity that maximizes the final behavior adoption size. Finally we find that the above phenomena are not qualitatively affected by the heterogeneous degree distribution. Our suggested theory agrees with the simulation results. Copyright © 2017, The Authors. All rights reserved.

关键词： Probability distributions

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Fraud Detection for MCC Manipulation

Fraud Detection for MCC Manipulation

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2016 International Conference on Informatics, Management engineering and Industrial Application (IMEIA 2016)

作者： Hong-feng CHAI Xin LIU Yan-jun XU Shuo HE Xia-xia LIN Zhen LIU Jin MA China Unionpay Limited Liability Company Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Big Data Center University of Electronic Science and Technology of China

MCC manipulation arising in recent years is a new kind of fraud happened in financial system. Aiming at this issue, this study proposes a computational framework to detect such fraud behavior. The framework utilizes hierarchical clustering to discover the inherent transaction behavior pattern existed in various businesses and predicts the fraud merchant by applying logistic regression model. The experimental results indicate that the model overall outperforms other classifiers in detecting merchant's MCC manipulation.

关键词： MCC Fraud detection Hierarchical clustering Logistic regression

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