The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
Paulo Shakarian - One of the best experts on this subject based on the ideXlab platform.
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WWW (Companion Volume) - Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks
Companion Proceedings of The 2019 World Wide Web Conference, 2019Co-Authors: Soumajyoti Sarkar, Hamidreza Alvari, Paulo ShakarianAbstract:Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a Susceptible Node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a Node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent Node of each potentially Susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
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Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks
arXiv: Social and Information Networks, 2019Co-Authors: Soumajyoti Sarkar, Hamidreza Alvari, Paulo ShakarianAbstract:Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a Susceptible Node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a Node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent Node of each potentially Susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
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Spreaders in the Network SIR Model: An Empirical Study
arXiv: Social and Information Networks, 2012Co-Authors: Brian Macdonald, Paulo Shakarian, Nicholas Howard, Geoffrey MooresAbstract:We use the Susceptible-infected-recovered (SIR) model for disease spread over a network, and empirically study how well various centrality measures perform at identifying which Nodes in a network will be the best spreaders of disease on 10 real-world networks. We find that the relative performance of degree, shell number and other centrality measures can be sensitive to b , the probability that an infected Node will transmit the disease to a Susceptible Node. We also find that eigenvector centrality performs very well in general for values of b above the epidemic threshold.
Wei Wang - One of the best experts on this subject based on the ideXlab platform.
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Irreversible contact process on complex networks with dynamical recovery probability
Physica A-statistical Mechanics and Its Applications, 2019Co-Authors: Shuang Zhang, Tao Wu, Wei WangAbstract:Abstract Contact process is widely used to describe the spreading of virus and information in real-world system. Previous studies on contact process always assumed that the infected Node uniformly contacts one neighbor and becomes recovery with a constant recovery probability. In this paper, we propose a novel irreversible spreading model, in which an infected Node preferencely contacts a neighbor according to the degrees of neighbors and becomes recovery with a dynamical probability. We propose a heterogeneous mean-field approach to describe the spreading dynamics. On both homogeneous and heterogeneous networks, we find that preferencely contacting the small degree Nodes can promote the spreading dynamics, while the spreading dynamics will be greatly suppressed when each Susceptible Node provides the same volume of resources to the infected Nodes. Our suggested theory can well predict the numerical simulations.
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Social contagions on multiplex networks with heterogeneous population
Physica A-statistical Mechanics and Its Applications, 2019Co-Authors: Jian-qun Wang, Zeng-ping Zhang, Wei WangAbstract:In this paper, we study the effects of heterogeneous population on the dynamics of social contagions on multiplex networks. We assume a fraction of f Nodes with a higher adoption threshold T>1, and the remaining fraction of 1−f Nodes with adoption threshold 1. A social contagion model is proposed to describe the social contagions, in which a Susceptible Node adopting the contagion only when its received accumulated information is larger than the adoption threshold in either subnetwork. With an edge-based compartmental approach and extensive numerical simulations, we find that the system exhibits a continuous phase transition for small values of f, while shows a hybrid phase transition for relatively large values of f and T. For homogeneous multiplex networks the hybrid phase transition occurs, while there is only a continuous phase transition for heterogeneous multiplex networks. Our theoretical predictions agree well with numerical simulations.
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Complex contagions with social reinforcement from different layers and neighbors
Physica A-statistical Mechanics and Its Applications, 2018Co-Authors: Ling-jiao Chen, Xiaolong Chen, Wei WangAbstract:Abstract Researches about complex contagions on complex networks always neglect the reinforcement effect from different layers and neighbors simultaneously. In this paper we propose a non-Markovian model to describe complex contagions in which a Susceptible Node becoming adopted must take the social reinforcement from different layers and neighbors into consideration. Through extensive numerical simulations we find that the final adoption size will increase sharply with the information transmission probability at a large adoption threshold. In addition, for small values of adoption threshold, a few seeds could trigger a global contagion. However, there is a critical seed size below which the global contagion becomes impossible for large values of adoption threshold. Besides that, we develop an edge-based compartmental (EBC) theory to describe the proposed model, and it agrees well with numerical simulations.
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Explosive spreading on complex networks: The role of synergy.
Physical review. E, 2017Co-Authors: Wei Wang, Ming Tang, Tao ZhouAbstract: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.
Soumajyoti Sarkar - One of the best experts on this subject based on the ideXlab platform.
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WWW (Companion Volume) - Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks
Companion Proceedings of The 2019 World Wide Web Conference, 2019Co-Authors: Soumajyoti Sarkar, Hamidreza Alvari, Paulo ShakarianAbstract:Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a Susceptible Node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a Node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent Node of each potentially Susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
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Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks
arXiv: Social and Information Networks, 2019Co-Authors: Soumajyoti Sarkar, Hamidreza Alvari, Paulo ShakarianAbstract:Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a Susceptible Node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a Node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent Node of each potentially Susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
Cecilia Metra - One of the best experts on this subject based on the ideXlab platform.
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Low-Cost Strategy to Mitigate the Impact of Aging on Latches’ Robustness
IEEE Transactions on Emerging Topics in Computing, 2018Co-Authors: Martin Omana, T. Edara, Cecilia MetraAbstract:Analyses recently presented in the literature have shown that the Bias Temperature Instability (BTI) ageing phenomenon may increase significantly the susceptibility to soft errors (SEs) of robust latches. Particularly, this is the case of low-cost robust latches, whose robustness is obtained by increasing the critical charge of their most Susceptible Node, that is the Node most contributing to the latch soft error rate (SER). Therefore, in applications mandating the use of low-cost robust latches, designers will have to face the problem of such latches’ robustness degradation during the IC operation. In order to cope with this problem, we here propose a strategy to reduce the impact of BTI on the SER of standard and low-cost robust latches. It will be proven that our approach enables to reduce by approximately the 50 percent the SER increase due to BTI during circuit lifetime with respect to original latches, at limited increase in terms of area overhead, latch setup time and power consumption, and with no impact on the latch input-output delay.
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Impact of Aging Phenomena on Latches’ Robustness
IEEE Transactions on Nanotechnology, 2016Co-Authors: Martin Omana, T. Edara, Daniele Rossi, Cecilia MetraAbstract:In this paper, we analyze the effects of aging mechanisms on the soft error susceptibility of both standard and robust latches. Particularly, we consider bias temperature instability (BTI) affecting both nMOS (positive BTI) and pMOS (negative BTI), which is considered the most critical aging mechanism threatening the reliability of ICs. Our analyses show that as an IC ages, BTI significantly increases the susceptibility of both standard latches and low-cost robust latches, whose robustness is based on the increase in the critical charge of their most Susceptible Node(s). Instead, we will show that BTI minimally affects the soft error susceptibility of more costly robust latches that avoid the generation of soft errors by design. Consequently, our analysis highlights the fact that in applications mandating the use of low-cost robust latches, designers will have to face the problem of their robustness degradation during IC lifetime. Therefore, for these applications, designers will have to develop proper low-cost solutions to guarantee the minimal required level of robustness during the whole IC lifetime.
Hamidreza Alvari - One of the best experts on this subject based on the ideXlab platform.
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WWW (Companion Volume) - Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks
Companion Proceedings of The 2019 World Wide Web Conference, 2019Co-Authors: Soumajyoti Sarkar, Hamidreza Alvari, Paulo ShakarianAbstract:Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a Susceptible Node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a Node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent Node of each potentially Susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
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Leveraging Motifs to Model the Temporal Dynamics of Diffusion Networks
arXiv: Social and Information Networks, 2019Co-Authors: Soumajyoti Sarkar, Hamidreza Alvari, Paulo ShakarianAbstract:Information diffusion mechanisms based on social influence models are mainly studied using likelihood of adoption when active neighbors expose a user to a message. The problem arises primarily from the fact that for the most part, this explicit information of who-exposed-whom among a group of active neighbors in a social network, before a Susceptible Node is infected is not available. In this paper, we attempt to understand the diffusion process through information cascades by studying the temporal network structure of the cascades. In doing so, we accommodate the effect of exposures from active neighbors of a Node through a network pruning technique that leverages network motifs to identify potential infectors responsible for exposures from among those active neighbors. We attempt to evaluate the effectiveness of the components used in modeling cascade dynamics and especially whether the additional effect of the exposure information is useful. Following this model, we develop an inference algorithm namely InferCut, that uses parameters learned from the model and the exposure information to predict the actual parent Node of each potentially Susceptible user in a given cascade. Empirical evaluation on a real world dataset from Weibo social network demonstrate the significance of incorporating exposure information in recovering the exact parents of the exposed users at the early stages of the diffusion process.
