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Moore JM, Small M, Yan G, Yang H, Gu C, Wang H. Network Spreading from Network Dimension. PHYSICAL REVIEW LETTERS 2024; 132:237401. [PMID: 38905697 DOI: 10.1103/physrevlett.132.237401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/01/2024] [Accepted: 05/01/2024] [Indexed: 06/23/2024]
Abstract
Continuous-state network spreading models provide critical numerical and analytic insights into transmission processes in epidemiology, rumor propagation, knowledge dissemination, and many other areas. Most of these models reflect only local features such as adjacency, degree, and transitivity, so can exhibit substantial error in the presence of global correlations typical of empirical networks. Here, we propose mitigating this limitation via a network property ideally suited to capturing spreading. This is the network correlation dimension, which characterizes how the number of nodes within range of a source typically scales with distance. Applying the approach to susceptible-infected-recovered processes leads to a spreading model which, for a wide range of networks and epidemic parameters, can provide more accurate predictions of the early stages of a spreading process than important established models of substantially higher complexity. In addition, the proposed model leads to a basic reproduction number that provides information about the final state not available from popular established models.
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Affiliation(s)
- Jack Murdoch Moore
- MOE Key Laboratory of Advanced Micro-Structured Materials, and School of Physical Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, MOE Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, People's Republic of China
| | - Michael Small
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Crawley 6009, Western Australia, Australia
- Mineral Resources, CSIRO, Kensington 6151, Western Australia, Australia
| | - Gang Yan
- MOE Key Laboratory of Advanced Micro-Structured Materials, and School of Physical Science and Engineering, Tongji University, Shanghai 200092, People's Republic of China
- National Key Laboratory of Autonomous Intelligent Unmanned Systems, MOE Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 200092, People's Republic of China
| | - Huijie Yang
- Business School, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai 200093, People's Republic of China
| | - Changgui Gu
- Business School, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai 200093, People's Republic of China
| | - Haiying Wang
- Business School, University of Shanghai for Science and Technology, 334 Jungong Road, Shanghai 200093, People's Republic of China
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Yin F, Wang J, Jiang X, Huang Y, Yang Q, Wu J. Modeling and analyzing an opinion network dynamics considering the environmental factor. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:16866-16885. [PMID: 37920038 DOI: 10.3934/mbe.2023752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
With the development of Internet technology, social media has gradually become an important platform where users can express opinions about hot events. Research on the mechanism of public opinion evolution is beneficial to guide the trend of opinions, making users' opinions change in a positive direction or reach a consensus among controversial crowds. To design effective strategies for public opinion management, we propose a dynamic opinion network susceptible-forwarding-immune model considering environmental factors (NET-OE-SFI), which divides the forwarding nodes into two types: support and opposition based on the real data of users. The NET-OE-SFI model introduces environmental factors from infectious diseases into the study of network information transmission, which aims to explore the evolution law of users' opinions affected by the environment. We attempt to combine the complex media environmental factors in social networks with users' opinion information to study the influence of environmental factors on the evolution of public opinion. Data fitting of real information transmission data fully demonstrates the validity of this model. We have also made a variety of sensitivity analysis experiments to study the influence of model parameters, contributing to the design of reasonable and effective strategies for public opinion guidance.
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Affiliation(s)
- Fulian Yin
- State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
- College of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Jinxia Wang
- College of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Xinyi Jiang
- College of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Yanjing Huang
- College of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Qianyi Yang
- College of Information and Communication Engineering, Communication University of China, Beijing 100024, China
| | - Jianhong Wu
- Laboratory for Industrial and Applied Mathematics, York University, Toronto M3J1P3, Canada
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Guo FY, Zhou JJ, Ruan ZY, Zhang J, Qi L. Hub-collision avoidance and leaf-node options algorithm for fractal dimension and renormalization of complex networks. CHAOS (WOODBURY, N.Y.) 2022; 32:123116. [PMID: 36587351 DOI: 10.1063/5.0113001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/09/2022] [Indexed: 06/17/2023]
Abstract
The box-covering method plays a fundamental role in the fractal property recognition and renormalization analysis of complex networks. This study proposes the hub-collision avoidance and leaf-node options (HALO) algorithm. In the box sampling process, a forward sampling rule (for avoiding hub collisions) and a reverse sampling rule (for preferentially selecting leaf nodes) are determined for bidirectional network traversal to reduce the randomness of sampling. In the box selection process, the larger necessary boxes are preferentially selected to join the solution by continuously removing small boxes. The compact-box-burning (CBB) algorithm, the maximum-excluded-mass-burning (MEMB) algorithm, the overlapping-box-covering (OBCA) algorithm, and the algorithm for combining small-box-removal strategy and maximum box sampling with a sampling density of 30 (SM30) are compared with HALO in experiments. Results on nine real networks show that HALO achieves the highest performance score and obtains 11.40%, 7.67%, 2.18%, and 8.19% fewer boxes than the compared algorithms, respectively. The algorithm determinism is significantly improved. The fractal dimensions estimated by covering four standard networks are more accurate. Moreover, different from MEMB or OBCA, HALO is not affected by the tightness of the hubs and exhibits a stable performance in different networks. Finally, the time complexities of HALO and the compared algorithms are all O(N2), which is reasonable and acceptable.
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Affiliation(s)
- Fei-Yan Guo
- School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China
| | - Jia-Jun Zhou
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
| | - Zhong-Yuan Ruan
- Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jian Zhang
- School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China
| | - Lin Qi
- School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China
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Abstract
In the real world, pathogens do not exist in isolation. The transmission of one pathogen may be affected by the presence of other pathogens, and certain pathogens generate multiple strains with different spreading features. Hence, the behavior of multi-pathogen transmission has attracted much attention in epidemiological research. In this paper, we use the pairwise approximation method to formulate two-pathogen models capturing cross-immunity, super-infection, and co-infection phenomena, in which each pathogen follows a susceptible-infected-susceptible (SIS) mechanism. For each model, we calculate the basic reproduction number and analyze the stability of equilibria, and discuss the differences from the mean-field approach. We demonstrate that simulations are in good agreement with the analytical results.
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