Article
Multidisciplinary Sciences
Douglas Guilbeault, Damon Centola
Summary: The study highlights the differences between complex contagions and simple contagions, showing that exposure to multiple peers may be necessary for adoption in complex contagions. Traditional measures of path length fail to accurately define network connectedness and node centrality for complex contagions. To address this issue, researchers have developed new measures of complex path length and complex centrality.
NATURE COMMUNICATIONS
(2021)
Article
Multidisciplinary Sciences
Zhihao Dong, Yuanzhu Chen, Terrence S. Tricco, Cheng Li, Ting Hu
Summary: The paper proposes a localized strategy to identify vital nodes without global knowledge of the network. Experimental results show that the average degree of the identified node set is 3-8 times higher than that of the full node set, and higher-degree nodes take larger proportions in the degree distribution of the identified vital node set.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Information Systems
Aybike Simsek
Summary: Epidemic modeling in complex networks has gained significant attention recently. The proposed Lexical Sorting Centrality (LSC) combines multiple centrality measures to accurately and efficiently determine the spreading ability of nodes in the network, outperforming other centrality measures in terms of accuracy, decisiveness, and speed.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Shen Zhong, Haotian Zhang, Yong Deng
Summary: The importance of research on complex networks is increasing, and identifying influential nodes remains an urgent and crucial issue. This paper proposes a Local Degree Dimension (LDD) approach that assesses the importance of nodes in complex networks by considering the increasing and decreasing rates of the numbers of each layer neighbor nodes. Experimental results demonstrate the effectiveness of LDD in accurately identifying influential nodes and quantifying their importance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Interdisciplinary Applications
Onur Ugurlu
Summary: This research focuses on the detection of critical nodes in complex networks and proposes a new centrality measure called Isolating Centrality. Experimental results on both synthetic and real-world networks show that degree-like centralities are more relevant than path-like centralities, and the proposed Isolating Centrality outperforms traditional measures in identifying critical nodes.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Physics, Multidisciplinary
Into Almiala, Henrik Aalto, Vesa Kuikka
Summary: This study introduces a novel model that accurately simulates influence spreading on complex networks with partial breakthrough effects. By controlling the breakthrough probability, the model can be applied in various fields such as social influence and epidemic spreading.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Shenglong Wang, Jing Yang, Xiaoyu Ding, Jianpei Zhang, Meng Zhao
Summary: Local community detection algorithms have grown rapidly due to their linear computing time and ease of accessing local information in real-world networks. However, challenges remain such as lacking a seed-oriented algorithm and quality dependence on seed position and predefined parameters. Therefore, a seed-oriented local community detection algorithm named SOLCD is proposed based on influence spreading, achieving high-quality seed-oriented local communities.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2022)
Article
Multidisciplinary Sciences
Haifeng Hu, Zejun Sun, Feifei Wang, Liwen Zhang, Guan Wang
Summary: This paper proposes an accurate and efficient algorithm for critical node mining, which determines influential nodes using both global and local information. The proposed method solves the limitations of existing methods. Experimental results demonstrate that the algorithm effectively explores influential nodes in complex networks.
SCIENTIFIC REPORTS
(2022)
Article
Mathematics, Interdisciplinary Applications
Qing Xu, Lizhu Sun, Changjiang Bu
Summary: This paper studies the influence of combining the first and second neighbors of a given node in a network. Similar to the one-step walk matrix, a three-order tensor, namely the two-steps tensor, is constructed to represent the first and second neighbor relation among nodes. The positive eigenvector of the two-steps tensor corresponding to its spectral radius is adopted as a new centrality measure, referred to as the two-steps eigenvector centrality, which extends the notion of eigenvector centrality. Experimental results show that the new centrality measure is effective and provides additional insights regarding node importance in certain networks.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Computer Science, Artificial Intelligence
Haotian Zhang, Shen Zhong, Yong Deng, Kang Hao Cheong
Summary: In this article, a novel centrality measure based on local fuzzy information centrality (LFIC) is proposed and its effectiveness is verified through multiple experiments. The results indicate that this method can identify influential nodes that cause wider scope of infection and larger effect on network connectivity. Furthermore, an extension method is proposed for weighted directed complex networks.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Mathematics, Applied
Esther Garcia, Miguel Romance
Summary: In this paper, the controllability of spectral centrality in complex networks is studied. The inverse problem of designing an unweighted graph with a prescribed centrality is considered. It is shown that for every possible ranking, including ties, there exists an unweighted directed/undirected complex network whose PageRank or eigenvector centrality yields the desired ranking. Different families of networks are presented to solve this problem analytically for directed/undirected graphs with/without loops.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2022)
Article
Multidisciplinary Sciences
Mohd Fariduddin Mukhtar, Zuraida Abal Abas, Azhari Samsu Baharuddin, Mohd Natashah Norizan, Wan Farah Wani Wan Fakhruddin, Wakisaka Minato, Amir Hamzah Abdul Rasib, Zaheera Zainal Abidin, Ahmad Fadzli Nizam Abdul Rahman, Siti Haryanti Hairol Anuar
Summary: Centrality analysis is a crucial tool for understanding nodes in a network, but different centrality measures lack unique information. To improve the identification of influential nodes, a new method called Hybrid-GSM (H-GSM) is proposed, which combines K-shell decomposition and Degree Centrality. H-GSM characterizes node impact more precisely than GSM and outperforms other approaches in computational complexity, node discrimination, and accuracy based on simulations of real-world networks using the SIR model. The findings demonstrate H-GSM as an effective method for identifying influential nodes in complex networks.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Caroline L. Alves, Rubens Gisbert Cury, Kirstin Roster, Aruane M. Pineda, Francisco A. Rodrigues, Christiane Thielemann, Manuel Ciba
Summary: This study investigates the impact of ayahuasca on brain activity using machine learning and complex network techniques. The results show that changes in connectivity between brain regions play a crucial role in detecting the effects of ayahuasca. The study also identifies the most activated brain regions and important brain connections associated with ayahuasca.
Article
Physics, Multidisciplinary
Jiaqi Song, Zhidan Feng, Xingqin Qi
Summary: Inspired by various applications, this study explores the ranking of node spreading ability in signed social networks and proposes a new method called local influence matrix (LIM) method to find the seed nodes set with maximum positive influence on a specific group of targets but with minimum influence on non-target nodes. The simulation results show that our method performs well on real networks.
FRONTIERS IN PHYSICS
(2022)
Article
Computer Science, Software Engineering
Levent Sabah, Mehmet Simsek
Summary: Determining the centrality of nodes in complex networks provides practical benefits in various areas, but there is no consensus on the definition of centrality. Different centrality measures have been developed, but they rank nodes in different orders. Recent research has focused on combining multiple centrality measures to consider different perspectives. This study proposes a fast and efficient method using the analytic hierarchy process and entropy weighting to combine multiple centrality measures. Experimental results show that the proposed method performs competitively in terms of computational speed and can be applied to large and dynamic networks.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
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)