Review
Green & Sustainable Science & Technology
Xiaoqian Sun, Sebastian Wandelt
Summary: Air transportation systems are crucial components of critical infrastructure in our interconnected world. Network science is increasingly used to study the resilience dynamics of air transportation, leading to a rich body of literature on designing resilient systems. This review paper summarizes the research from the past 15 years, highlighting the importance of understanding the state of the art to improve future air transportation resilience and sustainability.
Article
Computer Science, Information Systems
Ruizi Wu, Jie Huang, Zhuoran Yu, Junli Li
Summary: This paper introduces the Real-RP, a multi-convolutional neural network method, for predicting the robustness of real-world complex networks. Experimental results show high precision in network classification and lower prediction errors compared to existing methods.
Article
Physics, Multidisciplinary
Michele Bellingeri, Massimiliano Turchetto, Francesco Scotognella, Roberto Alfieri, Ngoc-Kim-Khanh Nguyen, Quang Nguyen, Davide Cassi
Summary: In this study, we simulate degree and betweenness node attacks on 200 real-world networks from different scientific fields, and evaluate the network damage and robustness. By analyzing the correlation with network structural indicators, we find that the Estrada heterogeneity index, which evaluates the degree difference of connected nodes, best predicts the network damage.
FRONTIERS IN PHYSICS
(2023)
Article
Mathematics
Jisha Mariyam John, Michele Bellingeri, Divya Sindhu Lekha, Davide Cassi, Roberto Alfieri
Summary: In this study, we investigate the effect of weight thresholding (WT) on the robustness of real-world complex networks. We find that real-world networks subjected to WT hold a robust connectivity structure to node attack even for higher WT values. Weighted node centralities are more stable indicators of node importance in real-world networks subjected to link sparsification compared to binary node centralities.
Article
Statistics & Probability
Nelson Antunes, Shankar Bhamidi, Tianjian Guo, Vladas Pipiras, Bang Wang
Summary: This work focuses on estimating the in-degree distribution of directed networks from sampling network nodes or edges. Two estimation approaches are proposed, based on inversion and asymptotic methods. The performance of these approaches is tested on synthetic and real networks, showing good results.
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Huan Luo, Stephanie German Paal
Summary: Small data sets present a challenge in machine learning, especially in regression scenarios, where a lack of relevant data can lead to biased models. This article proposes a novel regression-based transfer learning model that transfers knowledge from a large, relevant data set to a small data set, reducing bias and improving prediction performance. The proposed approach, DW-SVTR, significantly reduces the impact of small sample bias compared to standard ML methods, as demonstrated through numerical results.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Masaki Chujyo, Yukio Hayashi
Summary: This paper investigates the impact of adding links on network robustness and finds that adding links based on the minimum degree strategy effectively improves robustness, while the longest distance strategy is only the best choice when the number of added links is small. Enhancing global loops is more important for improving robustness compared to enhancing local loops.
Article
Physics, Multidisciplinary
Chris Jones, Karoline Wiesner
Summary: The article explores the relationship between degree distribution entropy and network robustness, finding that degree distribution entropy only sets a lower bound to robustness for randomly configured networks. It is shown that degree distribution entropy does not indicate robustness for networks with the same form of degree distribution, while remaining degree entropy and robustness have a positive monotonic relationship.
Article
Mathematics
Prasan Ratnayake, Sugandima Weragoda, Janaka Wansapura, Dharshana Kasthurirathna, Mahendra Piraveenan
Summary: This study introduces a new robustness measure called "fitness-incorporated average network efficiency" to capture the heterogeneity of nodes in complex networks. The measure is applied to networks with different topologies, such as scale-free and Erdos-Renyi random topologies, using a wireless sensor network simulator and two real-world networks, showing its effectiveness in evaluating network robustness.
Article
Green & Sustainable Science & Technology
Alessandra Cornaro, Daniele Grechi
Summary: This paper explores the behavior of the ERC measure, a local robustness measure, on the railway network in Lombardy, Italy, and analyzes the impacts of deactivating stations or journeys on the network's robustness. The numerical results show that the ERC measures effectively identify critical stations and journeys within the network structure and outperform classical topological metrics. The ERC measure can provide valuable information for rerouting traffic along alternative paths in case of failures or disruptions.
Article
Geography
Jeremy Auerbach, Hyun Kim
Summary: This paper introduces a robustness measurement method applicable to multiline networks to uncover potential vulnerabilities of network components. The effectiveness and applicability of these new robustness measures are demonstrated through comparisons with traditional indexes.
ANNALS OF THE AMERICAN ASSOCIATION OF GEOGRAPHERS
(2022)
Article
Economics
Siping Li, Yaoming Zhou, Tanmoy Kundu, Jiuh-Biing Sheu
Summary: This paper studies the spatiotemporal variation of the worldwide air transportation network induced by the COVID-19 pandemic in 2020. The analysis shows that network metrics undergo tremendous variations in a very short period after the pandemic declaration, with significant drops in flight numbers and connections. The passenger throughput's changing rate is highly correlated to confirmed cases' growth rate during the early period of the COVID-19 outbreak, and different countries exhibit diverse responses to the pandemic condition in air transport.
Article
Computer Science, Information Systems
Rong Xie, Mengting Jiang, Yuchen Wang
Summary: This paper proposes an improved method for constructing small-world networks by combining degree distribution and eigenvector criteria, and analyzes the impact of network topology on synchronization capability. It also presents a solution to the problem of suppressed synchronization capability due to uneven degree distribution. Furthermore, a method for building Enhanced Synchronization Small-World is introduced, which deletes connecting edges based on node degree and reconnects edges according to the eigenvector criterion. Experimental results demonstrate the effectiveness of the proposed solution in improving network topology and solving the network synchronization problem.
Article
Engineering, Mechanical
Guochao Wang, Shenzhou Zheng, Jun Wang
Summary: This study develops a novel financial price model by utilizing the voter dynamic system on the Watts-Strogtz small-world network and the random jump process to simulate the price fluctuation dynamics of financial markets. The effectiveness of the model is verified through comparing price returns with returns of important stock indexes, showing that the model can well simulate nonlinear fluctuation behaviors of real markets. Additionally, statistical behaviors, multifractal behaviors, and complexity behaviors of returns are explored through empirical methods.
NONLINEAR DYNAMICS
(2021)
Article
Physics, Multidisciplinary
Marco Tomassini
Summary: This study demonstrates that the robustness of real-world complex networks against deliberate attacks can be improved using network modification techniques. Two methods, edge rewiring and edge addition, are compared and both are found to be useful in reducing vulnerability of infrastructure networks. Edge rewiring leads to less vulnerable networks, but it is difficult to implement in real-world networks due to engineering and cost reasons. Edge addition, on the other hand, is easier to apply and effective against random edge failures and attacks targeting bridge edges.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Physics, Multidisciplinary
Wang Li-Na, Chen Bin, Zang Chen-Rui
CHINESE PHYSICS LETTERS
(2012)
Article
Physics, Multidisciplinary
Li-Na Wang, Bin Chen, Zai-Zai Yan
Article
Computer Science, Artificial Intelligence
Li-Na Wang, Gui-Min Tan, Chen-Rui Zang
Summary: Mining spatio-temporal data based on network method provides insights into the dynamic changes and interaction structure of mobile communication system, revealing system stability and key nodes for information transmission.
APPLIED INTELLIGENCE
(2022)
Correction
Computer Science, Artificial Intelligence
Li-Na Wang, Gui-Min Tan, Chen-Rui Zang
APPLIED INTELLIGENCE
(2022)
Article
Mathematics, Applied
L. N. Wang, G. M. Tan, C. R. Zang
Summary: This study aims to provide theoretical and decision-making support for congestion issues in mobile communication systems by analyzing the network of high-traffic events. By mapping mobile communication spatiotemporal data to a weighted network, the associations among the occurrence times of high-traffic events can be mined through topological analysis. The research findings indicate that the event synchronization network exhibits characteristics of a small-world network.
Article
Multidisciplinary Sciences
Li-Na Wang, Chen-Rui Zang, Yuan-Yuan Cheng
SN APPLIED SCIENCES
(2020)
Article
Physics, Multidisciplinary
Wang Li-Na, Cheng Yuan-Yuan, Zang Chen-Rui
ACTA PHYSICA SINICA
(2019)
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)