1
|
Zhao X, Hu K, Tao Y, Jin L, Shi L. The impact of dynamic linking on cooperation on complex networks. CHAOS (WOODBURY, N.Y.) 2024; 34:073130. [PMID: 38995990 DOI: 10.1063/5.0221942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 06/27/2024] [Indexed: 07/14/2024]
Abstract
In complex social systems, individual relationships and the surrounding environment are constantly changing, allowing individuals to interact on dynamic networks. This study aims to investigate how individuals in a dynamic network engaged in a prisoner's dilemma game adapt their competitive environment through random edge breaks and reconnections when faced with incomplete information and adverse local conditions, thereby influencing the evolution of cooperative behavior. We find that random edge breaks and reconnections in dynamic networks can disrupt cooperative clusters, significantly hindering the development of cooperation. This negative impact becomes more pronounced over larger time scales. However, we also observe that nodes with higher degrees of connectivity exhibit greater resilience to this cooperation disruption. Our research reveals the profound impact of dynamic network structures on the evolution of cooperation and provides new insights into the mechanisms of cooperation in complex systems.
Collapse
Affiliation(s)
- Xiaoqian Zhao
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
| | - Kaipeng Hu
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
| | - Yewei Tao
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
| | - Libin Jin
- Research Institute of Data Science, Shanghai Lixin University of Accounting and Finance, 201209 Shanghai, China
| | - Lei Shi
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China
| |
Collapse
|
2
|
Jia W, Lü L, Mariani MS, Dai Y, Jiang T. Toward Detecting Previously Undiscovered Interaction Types in Networked Systems. IEEE INTERNET OF THINGS JOURNAL 2022; 9:20422-20430. [PMID: 36415479 PMCID: PMC9647711 DOI: 10.1109/jiot.2022.3174086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/08/2022] [Accepted: 05/07/2022] [Indexed: 06/16/2023]
Abstract
Studying networked systems in a variety of domains, including biology, social science, and Internet of Things, has recently received a surge of attention. For a networked system, there are usually multiple types of interactions between its components, and such interaction-type information is crucial since it always associated with important features. However, some interaction types that actually exist in the network may not be observed in the metadata collected in practice. This article proposes an approach aiming to detect previously undiscovered interaction types (PUITs) in networked systems. The first step in our proposed PUIT detection approach is to answer the following fundamental question: is it possible to effectively detect PUITs without utilizing metadata other than the existing incomplete interaction-type information and the connection information of the system? Here, we first propose a temporal network model which can be used to mimic any real network and then discover that some special networks which fit the model shall a common topological property. Supported by this discovery, we finally develop a PUIT detection method for networks which fit the proposed model. Both analytical and numerical results show this detection method is more effective than the baseline method, demonstrating that effectively detecting PUITs in networks is achievable. More studies on PUIT detection are of significance and in great need since this approach should be as essential as the previously undiscovered node-type detection which has gained great success in the field of biology.
Collapse
Affiliation(s)
- Wenjie Jia
- School of Electronic Information and CommunicationsHuazhong University of Science and TechnologyWuhan430074China
| | - Linyuan Lü
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of ChinaChengdu611731China
- Yangtze Delta Region Institute, University of Electronic Science and Technology of ChinaHuzhou313001China
| | - Manuel Sebastian Mariani
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of ChinaChengdu611731China
- URPP Social NetworksUniversity of Zurich8050ZurichSwitzerland
| | - Yueyue Dai
- Research Center of 6G Mobile Communications, the School of Cyber Science and Engineering, and the Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
| | - Tao Jiang
- Research Center of 6G Mobile Communications, the School of Cyber Science and Engineering, and the Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
| |
Collapse
|
3
|
Torres JJ, Marro J. Physics Clues on the Mind Substrate and Attributes. Front Comput Neurosci 2022; 16:836532. [PMID: 35465268 PMCID: PMC9026167 DOI: 10.3389/fncom.2022.836532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 11/16/2022] Open
Abstract
The last decade has witnessed a remarkable progress in our understanding of the brain. This has mainly been based on the scrutiny and modeling of the transmission of activity among neurons across lively synapses. A main conclusion, thus far, is that essential features of the mind rely on collective phenomena that emerge from a willful interaction of many neurons that, mediating other cells, form a complex network whose details keep constantly adapting to their activity and surroundings. In parallel, theoretical and computational studies developed to understand many natural and artificial complex systems, which have truthfully explained their amazing emergent features and precise the role of the interaction dynamics and other conditions behind the different collective phenomena they happen to display. Focusing on promising ideas that arise when comparing these neurobiology and physics studies, the present perspective article shortly reviews such fascinating scenarios looking for clues about how high-level cognitive processes such as consciousness, intelligence, and identity can emerge. We, thus, show that basic concepts of physics, such as dynamical phases and non-equilibrium phase transitions, become quite relevant to the brain activity while determined by factors at the subcellular, cellular, and network levels. We also show how these transitions depend on details of the processing mechanism of stimuli in a noisy background and, most important, that one may detect them in familiar electroencephalogram (EEG) recordings. Thus, we associate the existence of such phases, which reveal a brain operating at (non-equilibrium) criticality, with the emergence of most interesting phenomena during memory tasks.
Collapse
|
4
|
Millán AP, Torres JJ, Johnson S, Marro J. Growth strategy determines the memory and structural properties of brain networks. Neural Netw 2021; 142:44-56. [PMID: 33984735 DOI: 10.1016/j.neunet.2021.04.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 03/04/2021] [Accepted: 04/20/2021] [Indexed: 11/18/2022]
Abstract
The interplay between structure and function affects the emerging properties of many natural systems. Here we use an adaptive neural network model that couples activity and topological dynamics and reproduces the experimental temporal profiles of synaptic density observed in the brain. We prove that the existence of a transient period of relatively high synaptic connectivity is critical for the development of the system under noise circumstances, such that the resulting network can recover stored memories. Moreover, we show that intermediate synaptic densities provide optimal developmental paths with minimum energy consumption, and that ultimately it is the transient heterogeneity in the network that determines its evolution. These results could explain why the pruning curves observed in actual brain areas present their characteristic temporal profiles and they also suggest new design strategies to build biologically inspired neural networks with particular information processing capabilities.
Collapse
Affiliation(s)
- Ana P Millán
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands.
| | - Joaquín J Torres
- Institute 'Carlos I' for Theoretical and Computational Physics, University of Granada, Spain
| | - Samuel Johnson
- School of Mathematics, University of Birmingham, Edgbaston B15 2TT, UK; Alan Turing Institute, London NW1 2DB, UK
| | - J Marro
- Institute 'Carlos I' for Theoretical and Computational Physics, University of Granada, Spain
| |
Collapse
|
5
|
Yuan Y, Liu J, Zhao P, Xing F, Huo H, Fang T. Structural Insights Into the Dynamic Evolution of Neuronal Networks as Synaptic Density Decreases. Front Neurosci 2019; 13:892. [PMID: 31507365 PMCID: PMC6714520 DOI: 10.3389/fnins.2019.00892] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Accepted: 08/08/2019] [Indexed: 11/13/2022] Open
Abstract
The human brain is thought to be an extremely complex but efficient computing engine, processing vast amounts of information from a changing world. The decline in the synaptic density of neuronal networks is one of the most important characteristics of brain development, which is closely related to synaptic pruning, synaptic growth, synaptic plasticity, and energy metabolism. However, because of technical limitations in observing large-scale neuronal networks dynamically connected through synapses, how neuronal networks are organized and evolve as their synaptic density declines remains unclear. Here, by establishing a biologically reasonable neuronal network model, we show that despite a decline in the synaptic density, the connectivity, and efficiency of neuronal networks can be improved. Importantly, by analyzing the degree distribution, we also find that both the scale-free characteristic of neuronal networks and the emergence of hub neurons rely on the spatial distance between neurons. These findings may promote our understanding of neuronal networks in the brain and have guiding significance for the design of neuronal network models.
Collapse
Affiliation(s)
- Ye Yuan
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Jian Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Peng Zhao
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Fu Xing
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Hong Huo
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| | - Tao Fang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China
| |
Collapse
|
6
|
Concurrence of form and function in developing networks and its role in synaptic pruning. Nat Commun 2018; 9:2236. [PMID: 29884799 PMCID: PMC5993834 DOI: 10.1038/s41467-018-04537-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Accepted: 05/03/2018] [Indexed: 02/07/2023] Open
Abstract
A fundamental question in neuroscience is how structure and function of neural systems are related. We study this interplay by combining a familiar auto-associative neural network with an evolving mechanism for the birth and death of synapses. A feedback loop then arises leading to two qualitatively different types of behaviour. In one, the network structure becomes heterogeneous and dissasortative, and the system displays good memory performance; furthermore, the structure is optimised for the particular memory patterns stored during the process. In the other, the structure remains homogeneous and incapable of pattern retrieval. These findings provide an inspiring picture of brain structure and dynamics that is compatible with experimental results on early brain development, and may help to explain synaptic pruning. Other evolving networks—such as those of protein interactions—might share the basic ingredients for this feedback loop and other questions, and indeed many of their structural features are as predicted by our model. How structure and function coevolve in developing brains is little understood. Here, the authors study a coupled model of network development and memory, and find that due to the feedback networks with some initial memory capacity evolve into heterogeneous structures with high memory performance.
Collapse
|
7
|
Zhang W, Li YS, Du P, Xu C, Hui PM. Phase transitions in a coevolving snowdrift game with costly rewiring. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 90:052819. [PMID: 25493846 DOI: 10.1103/physreve.90.052819] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2014] [Indexed: 06/04/2023]
Abstract
We propose and study a dissatisfied adaptive snowdrift game with a payoff parameter r that incorporates a cost for rewiring a connection. An agent, facing adverse local environment, may switch action without a cost or rewire an existing link with a cost a so as to attain a better competing environment. Detailed numerical simulations reveal nontrivial and nonmonotonic dependence of the frequency of cooperation and the densities of different types of links on a and r. A theory that treats the cooperative and noncooperative agents separately and accounts for spatial correlation up to neighboring agents is formulated. The theory gives results that are in good agreement with simulations. The frequency of cooperation f_{C} is enhanced (suppressed) at high rewiring cost relative to that at low rewiring cost when r is small (large). For a given value of r, there exists a critical value of the rewiring cost below which the system evolves into a phase of frozen dynamics with isolated noncooperative agents segregated from a cluster of cooperative agents, and above which the system evolves into a connected population of mixed actions with continual dynamics. The phase boundary on the a-r phase space that separates the two phases with distinct structural, population and dynamical properties is mapped out. The phase diagram reveals that, as a general feature, for small r (small a), the disconnected and segregated phase can survive over a wider range of a(r).
Collapse
Affiliation(s)
- W Zhang
- Department of Electronics and Communication Engineering, Suzhou Institute of Industrial Technology, Suzhou, 215104, China
| | - Y S Li
- College of Physics, Optoelectronics and Energy, Soochow University, Suzhou, 215006, China
| | - P Du
- College of Physics, Optoelectronics and Energy, Soochow University, Suzhou, 215006, China
| | - C Xu
- College of Physics, Optoelectronics and Energy, Soochow University, Suzhou, 215006, China
| | - P M Hui
- Department of Physics and Institute of Theoretical Physics, Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| |
Collapse
|
8
|
Díaz MB, Porter MA, Onnela JP. Competition for popularity in bipartite networks. CHAOS (WOODBURY, N.Y.) 2010; 20:043101. [PMID: 21198071 DOI: 10.1063/1.3475411] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
We present a dynamical model for rewiring and attachment in bipartite networks. Edges are placed between nodes that belong to catalogs that can either be fixed in size or growing in size. The model is motivated by an empirical study of data from the video rental service Netflix, which invites its users to give ratings to the videos available in its catalog. We find that the distribution of the number of ratings given by users and that of the number of ratings received by videos both follow a power law with an exponential cutoff. We also examine the activity patterns of Netflix users and find bursts of intense video-rating activity followed by long periods of inactivity. We derive ordinary differential equations to model the acquisition of edges by the nodes over time and obtain the corresponding time-dependent degree distributions. We then compare our results with the Netflix data and find good agreement. We conclude with a discussion of how catalog models can be used to study systems in which agents are forced to choose, rate, or prioritize their interactions from a large set of options.
Collapse
Affiliation(s)
- Mariano Beguerisse Díaz
- Centre for Integrative Systems Biology, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
| | | | | |
Collapse
|