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ICTNET at TREC 2017 Dynamic Domain Track 26

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ICTNET at TREC 2017 Dynamic Domain Track

26th Text REtrieval Conference, TREC 2017

作者： Zhang, Weimin Hu, Yaokang Jia, Rongqian Wang, Xianfa Zhang, Le Feng, Yue Yu, Sihao Xue, Yuanhai Yu, Xiaoming Liu, Yue Cheng, Xueqi Institute of Computing Technology CAS China Key Laboratory of Web Data Science and Technology CAS China University of Chinese Academy of Sciences China

The effect of non-Markovian recovery on reversible failure propagation on networks

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arXiv 2019年

作者： Lin, Zhaohua Feng, Mi Tang, Ming Liu, Zonghua Hui, Pak Ming Lai, Ying-Cheng Department of Physics East China Normal University Shanghai200062 China School of Information Science Technology East China Normal University Shanghai200241 China Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Department of Physics Chinese University of Hong Kong Shatin Hong Kong School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

Compared with the classical irreversible failure propagation, the spontaneous recovery model can better depict the failure propagation on network systems such as financial and transportation networks. In this paper, we study how the non-Markovian recovery process influences cascading failure dynamics in the spontaneous recovery systems. To this end, we compare two kinds of failure-recovery models: the non-Markovian recovery (NMR) model and the corresponding Markovian recovery (MR) model. We first develop a Pairwise approximation theory for the MR and NMR models, which can predict the time evolution, steady state, and hysteresis behaviour of failure-recovery systems more accurately than the classical mean-field (MF) method. We find that although the non-Markovian recovery mechanism does not essentially affect the steady state and hysteresis behaviour of failure-recovery systems, the two models exhibit distinct evolution processes. When investigating the effect of initial conditions on the phase transition and hysteresis behavior, we find in the hysteresis region, the NMR model exhibits a non-monotonic growth characteristic: with the increase of initial failed nodes, the steady state staying in relative low-failure phase first turns into relative high-failure phase, and then into relative low-failure phase again. We then use the MF theory to explain the phenomenon qualitatively and point out when considering non-Markovian dynamics, we should study the steady state not only from the perspective of the variation of parameters, but also from the initial conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Hysteresis

Epidemic spreading on time-varying multiplex networks

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Physical Review E 2018年第6期98卷 062303-062303页

作者： Quan-Hui Liu Xinyue Xiong Qian Zhang Nicola Perra 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 Laboratory for the Modelling of Biological and Socio-technical Systems Northeastern University Boston Massachusetts 02115 USA Centre for Business Network Analysis University of Greenwich Park Row London SE10 9LS United Kingdom

Social interactions are stratified in multiple contexts and are subject to complex temporal dynamics. The systematic study of these two features of social systems has started only very recently, mainly thanks to the development of multiplex and time-varying networks. However, these two advancements have progressed almost in parallel with very little overlap. Thus, the interplay between multiplexity and the temporal nature of connectivity patterns is poorly understood. Here, we aim to tackle this limitation by introducing a time-varying model of multiplex networks. We are interested in characterizing how these two properties affect contagion processes. To this end, we study susceptible-infected-susceptible epidemic models unfolding at comparable timescale with respect to the evolution of the multiplex network. We study both analytically and numerically the epidemic threshold as a function of the multiplexity and the features of each layer. We found that higher values of multiplexity significantly reduce the epidemic threshold especially when the temporal activation patterns of nodes present on multiple layers are positively correlated. Furthermore, when the average connectivity across layers is very different, the contagion dynamics is driven by the features of the more densely connected layer. Here, the epidemic threshold is equivalent to that of a single layered graph and the impact of the disease, in the layer driving the contagion, is independent of the multiplexity. However, this is not the case in the other layers where the spreading dynamics is sharply influenced by it. The results presented provide another step towards the characterization of the properties of real networks and their effects on contagion phenomena.

关键词： Epidemic Network formation & growth Spreading Evolving networks Multilayer & multiplex networks

Impacts of opinion leaders on social contagions

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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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Epidemic spreading on time-varying multiplex networks

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arXiv 2018年

作者： Liu, Quan-Hui Xiong, Xinyue Zhang, Qian Perra, Nicola Web Science Center 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 the Modelling of Biological and Socio-technical Systems Northeastern University BostonMA02115 United States Centre for Business Network Analysis University of Greenwich Park Row LondonSE10 9LS United Kingdom

Social interactions are stratified in multiple contexts and are subject to complex temporal dynamics. The systematic study of these two features of social systems has started only very recently mainly thanks to the development of multiplex and time-varying networks. However, these two advancements have progressed almost in parallel with very little overlap. Thus, the interplay between multiplexity and the temporal nature of connectivity patterns is poorly understood. Here, we aim to tackle this limitation by introducing a time-varying model of multiplex networks. We are interested in characterizing how these two properties affect contagion processes. To this end, we study SIS epidemic models unfolding at comparable time-scale respect to the evolution of the multiplex network. We study both analytically and numerically the epidemic threshold as a function of the overlap between, and the features of, each layer. We found that, the overlap between layers significantly reduces the epidemic threshold especially when the temporal activation patterns of overlapping nodes are positively correlated. Furthermore, when the average connectivity across layers is very different, the contagion dynamics are driven by the features of the more densely connected layer. Here, the epidemic threshold is equivalent to that of a single layered graph and the impact of the disease, in the layer driving the contagion, is independent of the overlap. However, this is not the case in the other layers where the spreading dynamics are sharply influenced by it. The results presented provide another step towards the characterization of the properties of real networks and their effects on contagion phenomena. Copyright © 2018, The Authors. All rights reserved.

关键词： Time varying networks

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

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

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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

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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Orderness predicts academic performance: Behavioral analysis on campus lifestyle

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arXiv 2017年

作者： Cao, Yi Gao, Jian Lian, Defu Rong, Zhihai Shi, Jiatu Wang, Qing Wu, Yifan Yao, Huaxiu Zhou, Tao CompleX Lab Web Sciences Center University of Electronic Science and Technology of China Chengdu611731 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Key Laboratory for NeuroInformation of Ministry of Education School of Life Science and Technology Center for Information in Medicine University of Electronic Science and Technology of China Chengdu611731 China

Quantitative understanding of relationships between students' behavioral patterns and academic performances is a significant step towards personalized education. In contrast to previous studies that mainly based on questionnaire surveys, in this paper, we collect behavioral records from 18,960 undergraduate students' smart cards and propose a novel metric, called orderness, which measures the regularity of campus daily life (e.g., meals and showers) of each student. Empirical analysis demonstrates that academic performance (GPA) is strongly correlated with orderness. Furthermore, we show that orderness is an important feature to predict academic performance, which remarkably improves the prediction accuracy even at the presence of students' diligence. Based on these analyses, education administrators could better guide students' campus lives and implement effective interventions in an early stage when necessary. Copyright © 2017, The Authors. All rights reserved.

关键词： Students