Shi, XiaoqiuLong, WeiLi, YanyanDeng, Dingshan...
12页查看更多>>摘要：A supply chain system can be considered as an interdependent supply chain network (ISCN) which consists of an undirected cyber-network and a directed physical-network. To survive against disruptions, an ISCN needs to maintain operations and connectedness, referred to as robustness. Studies on the robustness of ISCNs when considering both functional and structural cascading failures are still scarce. In this paper, we first propose a cascading failure model which considers these two cascading failures simultaneously. We also present a model to generate ISCNs with different network types and interconnecting patterns. Using the transition threshold based on the proposed all-type connected sub-network, we can evaluate the robustness of ISCNs more properly. We then conduct numerical simulations to investigate how some parameters (e.g., network type, interconnecting pattern, the distribution of different types of nodes, etc.) affect the robustness of ISCNs under random and targeted disruptions. The results mainly show that the robustness of ISCNs can be affected seriously by different network types, interconnecting patterns, and disruption types; and the distribution of different types of nodes is more uniform, the corresponding ISCN is more robust, no matter what the disruption type is. Our results may provide help for building robust ISCNs. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:
NSTL
Elsevier
Kim, MinjungKim, Beom Jun
8页查看更多>>摘要："Too-big-to-fail (TBTF)"is a controversial approach to reducing the risk of cascading failures in the financial systems. In the TBTF defense strategy, most financial supports are provided to very big companies in order to avoid the complete breakdown of the entire system. We also consider "too-small-to-fail (TSTF)"as a comparative defense strategy, in which financial supports are more focused on small companies instead. We use two types of model network and a real network based on inter-industry Input-Output Table as underlying structures for cascading failures, and examine the validity of both defense strategies with two types of bailout policy, indirect (node capacity is increased) and direct (node load is decreased). We evaluate and compare the performances of TBTF and TSTF strategies in preventing cascading failures, and demonstrate that TSTF performs better when the node capacity is increased whereas TBTF works better when the node load is decreased. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Yin, YongChen, JinquLiu, JiePeng, Qiyuan...
16页查看更多>>摘要：Existing studies seldom consider network structure and passenger travel demand jointly, and certain impractical assumptions are generally considered for assessing the resilience of an urban rail transit (URT) network. To address the abovementioned limitations, we have proposed a performance indicator called the demand-impedance (DI) indicator, in which demand and impedance are reflected by passenger trips and travel time. By considering effective travel paths (ETPs) and passengers' path choice behavior, we have proposed a node centrality called effective path betweenness (EPB) by modifying the betweenness centrality (BC) to evaluate the importance of stations. The performance curve of a URT network during the attack and repair processes is depicted using the DI indicator, and a modified resilience metric is formulated by referring to the resilience triangle. The model application in the Chengdu subway network demonstrates that the correlation coefficient between the EPB and BC of stations is 0.901, which indicates that stations with a higher EPB are inclined to have a higher BC. The Chengdu subway network demonstrates a higher resilience under random disturbances than it does under malicious disturbances. Disturbance duration, passengers' tolerance time, and rescue ability on the Chengdu subway network significantly affect its resilience. Several practical suggestions involving the management of disturbances, shortening the emergency response time, providing passenger services, and improving emergency rescue ability are provided for managing the Chengdu subway system under disturbances. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Zhang, PanpanWang, TiandongYan, Jun
14页查看更多>>摘要：PageRank (PR) is a fundamental tool for assessing the relative importance of the nodes in a network. In this paper, we propose a measure, weighted PageRank (WPR), extended from the classical PR for weighted, directed networks with possible non-uniform node-specific information that is dependent or independent of network structure. A tuning parameter leveraging node degree and strength is introduced. An efficient algorithm based on R program has been developed for computing WPR in large-scale networks. We have tested the proposed WPR on widely used simulated network models, and found it outperformed the classical PR. Additionally, we apply the proposed WPR to the real network data generated from World Input-Output Tables as an example, and have seen the results that are consistent with the global economic trends, which renders it a preferred measure in the analysis. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Chen, Yu-RongZhang, Xian-XiaYu, Yin-ShengMa, Shi-Wei...
10页查看更多>>摘要：In this paper, we investigate the effect of direction preference on self-propelled agents. In the well-known Vicsek model, an agent treats its neighbors equally and updates its direction by the average direction of its neighbors. Whereas, in this study, agents prefer to synchronize with their neighbors moving in a certain direction, which is called preference direction. The center agent judges the influence value from its neighbor according to the direction angle between them. We assume that there exists a preference direction angle beta. When the direction angle between the neighbor and the center agent is closer to beta, the neighbor has greater influence on the center agent. The modified Vicsek model with preference direction angle is called direction preference model. We use the parameter alpha (0 < alpha <= 1) to adjust the effect of direction preference. The larger the value of alpha, the weaker the effect of direction preference. If alpha = 1, the direction preference will lose its effect. In the simulation experiments, different values of beta and alpha are discussed. Simulation results demonstrate that in noise-free environment the direction preference model with optimal beta = 3.7 pi /8 can accelerate the synchronization speed; in noise environment the direction preference model with beta be in [3 pi/8, 5 pi /8] has stronger robustness than the original Vicsek model and the optimal value of beta is altered. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Sheikh, Muhammad SameerRegan, Amelia
21页查看更多>>摘要：Traffic incidents due to non-recurring congestion frequently occur in urban environments. In this study, we propose the estimation and detection of traffic incidents based on independent component analysis (ICA) and hybrid observer (HO)-generalized likelihood ratio (GLR) techniques. First, we develop the traffic time series to obtain insight into the traffic flow and to detect traffic incidents. Then, we use time series analysis to construct complex networks. Next, we propose the ICA technique to monitor traffic flow. Then, we introduce a piecewise switched linear model based observer to estimate the possible occurrence of traffic incidents. Finally, we propose a new incident detection method that combines HO and GLR techniques. The combined HO-GLR method can produce better incident detection, improve traffic safety, and enhance traffic management systems. We have validated the effectiveness of the proposed method using simulated traffic data generated from the Ayer Rajah Expressway in Singapore and a real-world dataset from the I-880 freeway of California. The performance metrics used to evaluate the performance of the proposed method includes detection rate, false alarm rate, classification rate, mean time to detection and the area under receiving operating characteristics curve. The experimental results show that the proposed method has obtained better performance in all of the criteria when compared with other well-known methods. (C) 2021 The Author(s). Published by Elsevier B.V.
原文链接:- NSTL
- Elsevier
Lotf, Jalil JabariAzgomi, Mohammad AbdollahiDishabi, Mohammad Reza Ebrahimi
13页查看更多>>摘要：Over the recent decade, much research has been conducted in the field of social networks. The structure of these networks has been irregular, complex, and dynamic, and certain challenges such as network topology, scalability, and high computational complexities are typically evident. Because of the changes in the structure of social networks over time and the widespread diffusion of ideas, seed sets also need to change over time. Since there have been limited studies on highly dynamical changes in real networks, this research intended to address the network dynamicity in the classical influence maximization problem, which discovers a small subset of nodes in a social network and maximizes the influence spread. To this end, we used soft computing methods (i.e., a dynamic generalized genetic algorithm) in social networks under independent cascade models to obtain a dynamic seed set. We modeled several graphs in a specified timestamp through which the edges and the nodes changed within different time intervals. Attempts were made to find influential individuals in each of these graphs and maximize individuals' influences in social networks, which could thereby lead to changes in the members of the seed set. The proposed method was evaluated using standard datasets. The results showed that due to the reduction of the search areas and competition, the proposed method has higher scalability and accuracy to identify influential nodes in these snapshot graphs as compared with other comparable algorithms. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
Chen, WenhaoLi, JichaoJiang, JiangChen, Gang...
13页查看更多>>摘要：The problem of network disintegration is one of the core topics in the field of network science. Currently, most of the existing research is based on homogeneous and single layer networks of nodes. However, the various components of complex systems in the real world are often interdependent, and the cost of attacking different units is diverse, causing the traditional network disintegration method to lack good applicability. This paper establishes a weighted interdependent network (WIN) model, and based on this, a WIN disintegration strategy based on Q-learning is proposed. First, the network nodes are divided into multiple node sets according to the dependencies between the nodes, and the state and action space of Q-learning are determined. Next, the disintegration cost constraints and Q-learning parameters are defined to perform iterative learning. Then, the optimal network disintegration strategy is calculated according to the iterative Q-table. The results show that when the cost sensitivity factor (p) is fixed, DSQ can maintain good results in disintegrating different types of networks under different cost constraints, while the baseline methods have difficulty guaranteeing the disintegration effect in the face of different types of networks. Furthermore, we perform a sensitivity analysis on the p value and find that the effect of most of the baseline methods worsens as the p value increases, while DSQ maintains a good effect. (C) 2021 Elsevier B.V. All rights reserved.
原文链接:- NSTL
- Elsevier
