scholarly journals Impact of information diffusion on epidemic spreading in partially mapping two-layered time-varying networks

2021 ◽  
Vol 105 (4) ◽  
pp. 3819-3833
Author(s):  
Haili Guo ◽  
Qian Yin ◽  
Chengyi Xia ◽  
Matthias Dehmer
Keyword(s):  
Information Diffusion ◽  
Time Varying ◽  
Epidemic Spreading ◽  
Time Varying Networks

scholarly journals Epidemic spreading in modular time-varying networks

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Matthieu Nadini ◽  
Kaiyuan Sun ◽  
Enrico Ubaldi ◽  
Michele Starnini ◽  
Alessandro Rizzo ◽  
...  
Keyword(s):  
Time Varying ◽  
Epidemic Spreading ◽  
Time Varying Networks

scholarly journals Epidemic Processes Over Time-Varying Networks

2018 ◽  
Vol 5 (3) ◽  
pp. 1322-1334 ◽  
Author(s):  
Philip E. Pare ◽  
Carolyn L. Beck ◽  
Angelia Nedic
Keyword(s):  
Time Varying ◽  
Time Varying Networks ◽  
Over Time

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

Strategy evolution of information diffusion under time-varying user behavior in generalized networks

2017 ◽  
Vol 100 ◽  
pp. 91-103 ◽  
Author(s):  
Eleni Stai ◽  
Vasileios Karyotis ◽  
Antonia-Chrysanthi Bitsaki ◽  
Symeon Papavassiliou
Keyword(s):  
Information Diffusion ◽  
User Behavior ◽  
Time Varying ◽  
Generalized Networks

scholarly journals Distributed Constrained Stochastic Subgradient Algorithms Based on Random Projection and Asynchronous Broadcast over Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Junlong Zhu ◽  
Ping Xie ◽  
Qingtao Wu ◽  
Mingchuan Zhang ◽  
Ruijuan Zheng ◽  
...  
Keyword(s):  
Information Exchange ◽  
Optimal Solution ◽  
Random Projection ◽  
Learning Rate ◽  
Time Varying ◽  
Optimal Value ◽  
Constraint Set ◽  
Time Varying Networks ◽  
Subgradient Algorithms ◽  
Asymptotic Upper Bound

We consider a distributed constrained optimization problem over a time-varying network, where each agent only knows its own cost functions and its constraint set. However, the local constraint set may not be known in advance or consists of huge number of components in some applications. To deal with such cases, we propose a distributed stochastic subgradient algorithm over time-varying networks, where the estimate of each agent projects onto its constraint set by using random projection technique and the implement of information exchange between agents by employing asynchronous broadcast communication protocol. We show that our proposed algorithm is convergent with probability 1 by choosing suitable learning rate. For constant learning rate, we obtain an error bound, which is defined as the expected distance between the estimates of agent and the optimal solution. We also establish an asymptotic upper bound between the global objective function value at the average of the estimates and the optimal value.

Sign in / Sign up

Export Citation Format

Share Document