Complex systems and network science: a survey

Yang, Kewei; Li, Jichao; Liu, Maidi; Lei, Tianyang; Xu, Xueming; Wu, Hongzhen; Cao, Jiaping; Qi, Gaoxin

doi:10.23919/jsee.2023.000080

Cited by 16 publications

(4 citation statements)

References 251 publications

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“…Consider a multilayer network consisting of N nodes and M single-layer networks. The multilayer network is represented as G (G [1] , G [2] , G [3] , ...G [α] , ..., G [M] ), where each graph G [α] is composed of N identical nodes V v i ; i 1, 2, 3, /, N { }and edges describing the connection of nodes. Assume that these Frontiers in Physics frontiersin.org edges are undirected, and the nodes in each layer are selected from the same node set.…”

Section: The Multidimensional Data Transmission Model Based On Multil...mentioning

confidence: 99%

“…Equations 7-9 fit the game details between "roles" from a micro perspective, while the cooperation effect of multi-layers from a macro perspective requires the introduction of the concept of auxiliary layer and core layer, where the core layer is the simulation layer with attention weights. The evolution model of the auxiliary layer and the core aggregation layer is shown in Figure 3, where G [core] is the core layer, and G [1] , G [2] , G [3] is the cooperative evolution auxiliary layer. The node attributes, link weights, and dynamic games between the auxiliary layers are different.…”

Section: Multi-dimensional Information Sharing Based On Attention Mec...mentioning

confidence: 99%

“…In order to effectively analyze and manage IoT systems, complex network theory provides a powerful tool. Complex networks are networks composed of a large number of nodes and edges, which can have different attributes and functions, and the structure and dynamics of the networks have complex characteristics, such as small-world effect, scale-free property, community structure and so on [3]. Complex networks can describe various entities and relationships in IoT, such as devices, data, services, users and so on.…”

Section: Introductionmentioning

confidence: 99%

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Research on multi-layer network routing selection strategy based on cooperative evolutionary game in IoT environment

Wang,

Su,

Yang

et al. 2023

Front. Phys.

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As a new technology and application mode, the Internet of Things has an important impact on social life and economic development. In recent years, low-cost optimization of network transmission to solve the congestion problem of multi-layer communication networks has become one of the research hotspots. In this paper, a multi-dimensional communication data transmission model based on a multi-layer network is proposed. It then uses cooperative evolutionary game theory to calculate revenue, update weights, and adapt neighbors. Finally, the attention mechanism is dynamically introduced to share the weights of the multi-layer network, and the multi-dimensional communication propagation and routing strategies in the Internet of Things are studied and analyzed. The experimental results show that the model proposed in this paper has higher game revenue and application value than traditional single-layer network game theory. In particular, the indicators of cooperation rate, stable state, and maximum cooperation rate are better than the latter. The research results of this paper have important reference value for solving the problems of cooperation dilemma, social stickiness, and synergy in multi-layer networks.

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Section: The Multidimensional Data Transmission Model Based On Multil...mentioning

confidence: 99%

Section: Multi-dimensional Information Sharing Based On Attention Mec...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Research on multi-layer network routing selection strategy based on cooperative evolutionary game in IoT environment

Wang,

Su,

Yang

et al. 2023

Front. Phys.

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“…Complex networks abstract things graphically, which can help us understand complex systems from the perspective of the topology of interacting networks [17]. Causal inference, as a science in which the main goal is to discover causal relationships behind variables/things, is essential for rigorous decision making in the study of complex systems [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference

Wu,

Lu,

et al. 2023

Applied Sciences

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In complex systems constrained by multiple factors, it is very important to identify the key influencing factors for mastering the evolution and development law of a system and for obtaining scientific decision-making suggestions or schemes. At present, the method based on experimental simulation is limited by the difficulty of system model construction; DEMATEL (Factual Decision Trial and Evaluation Laboratory) is inevitably influenced by subjective factors. In view of this, we propose a novel model based on heuristic causal inference. By combining the network analysis in complex network science, the model defines the global/local causal pathway and the causal pathway’s length in the causal network and takes the causal pathway contribution degree as an indicator to measure the approximate causal effects. The model includes steps such as causal network learning, causal pathway contribution degree calculation, and key influencing factor identification. The model uses the Fast Causal Inference (FCI) algorithm with prior knowledge to learn the global causal network of the complex system and uses the heuristic causal inference to calculate the causal pathway contribution degree. The heuristic method draws on the idea of complex network topology analysis and measures the influence degree between variables by the number and distance of causal pathways. The key influencing factors are finally identified according to the causal pathway contribution degree. Based on the SECOM dataset, we carried out simulation experiments and demonstrated the feasibility and effectiveness of the proposed method.

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Analysis of Networking Within a Complex Technological Ecosystem

Wang

2024

Smart Innovation, Systems and Technologies

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Complex systems and network science: a survey

Cited by 16 publications

References 251 publications

Research on multi-layer network routing selection strategy based on cooperative evolutionary game in IoT environment

Research on multi-layer network routing selection strategy based on cooperative evolutionary game in IoT environment

Key Influencing Factors Identification in Complex Systems Based on Heuristic Causal Inference

Analysis of Networking Within a Complex Technological Ecosystem

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