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An effective edge-adding strategy for enhancing network traffic capacity

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  • Ma, Jinlong
  • Kong, Lingkang
  • Li, Hui-Jia

Abstract

With the rapid development of technology, the number of packets that need to be transmitted in the network is increasing, which leads to network congestion and reduces the user experience. Therefore, it is necessary to adjust the transmission path of packets in the network to improve the transmission threshold of packets in the network. In this paper, we abstract the real world network into a scale-free network for research, using nodes to represent individuals in reality, and edges to represent connections between individuals. Scale-free networks have serious inhomogeneity. Nodes in scale-free networks cannot be connected to all nodes in the network, which makes some gaps in the network. “Structural hole” describes the gaps in the network, which make some nodes have certain controllability to the whole network. But it only considers the relationship between nodes and their neighbors, without considering the overall structure of the whole network topology. K-shell decomposition algorithm can efficiently and accurately identify the location of nodes in the network. Therefore, K-shell decomposition algorithm is a global index. However, it still has shortcomings that does not consider the topological relationship between neighbors. Both structural hole and K-shell theory can characterize the importance of nodes. The combination of the two can make up for each other’s flaws and make the importance of nodes in the network more even. The more even the node importance, the more even the load of the node, the greater the increase in network traffic capacity. In this paper, we propose a network edge-adding strategy combining the structural hole theory and the k-shell algorithm to improve the traffic capacity. Extensive simulations have been performed to estimate the effectiveness of the proposed method under the efficient routing strategy. According to the simulation results, we can see that when the network size is fixed, regardless of the average degree of nodes, our proposed strategy improves the network traffic capacity, reduces the maximum betweenness centrality of nodes, and makes the load of nodes in the network more average. At the same time, our strategy improves the robustness of the network.

Suggested Citation

  • Ma, Jinlong & Kong, Lingkang & Li, Hui-Jia, 2023. "An effective edge-adding strategy for enhancing network traffic capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
  • Handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122008792
    DOI: 10.1016/j.physa.2022.128321
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    References listed on IDEAS

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    1. Shuai Zhang & Man-Gui Liang & Zhong-Yuan Jiang & Jia-Jing Wu, 2014. "Effective strategy of adding links for improving network transport efficiency on complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 25(06), pages 1-14.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 1999. "Diameter of the World-Wide Web," Nature, Nature, vol. 401(6749), pages 130-131, September.
    3. Lin, Yi & Zhang, Jianwei & Yang, Bo & Liu, Hong & Zhao, Liping, 2019. "An optimal routing strategy for transport networks with minimal transmission cost and high network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 551-561.
    4. Wang, Kai & Zhang, Yifeng & Zhou, Siyuan & Pei, Wenjiang & Wang, Shaoping & Li, Tao, 2011. "Optimal routing strategy based on the minimum information path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(13), pages 2593-2600.
    5. Yiguang Bai & Sanyang Liu & Zhaohui Zhang, 2017. "Effective hybrid link-adding strategy to enhance network transport efficiency for scale-free networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(08), pages 1-13, August.
    6. Yoshida, Akinori & Shimada, Yutaka & Kimura, Takayuki, 2021. "Efficient routing strategy with transmission history information and its surrogate analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    7. Pu, Cun-Lai & Zhou, Si-Yuan & Wang, Kai & Zhang, Yi-Feng & Pei, Wen-Jiang, 2012. "Efficient and robust routing on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 866-871.
    8. Wang, Jun & Zhang, Qian-Ming & Zhou, Tao, 2019. "Tag-aware link prediction algorithm in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 105-111.
    9. Liu, Wei & Chang, Zhenhai & Jia, Caiyan & Zheng, Yimei, 2022. "A generative node-attribute network model for detecting generalized structure and semantics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    10. Jiang, Zhong-Yuan & Liang, Man-Gui & An, Wen-Juan, 2014. "Effects of efficient edge rewiring strategies on network transport efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 379-385.
    11. Wu, Jian-Jun & Gao, Zi-You & Sun, Hui-jun, 2008. "Optimal traffic networks topology: A complex networks perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 1025-1032.
    12. Kumari, Suchi & Saroha, Abhishek & Singh, Anurag, 2020. "Efficient edge rewiring strategies for enhancement in network capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    13. Yang, Xiaoxia & Yang, Xiaoli & Pan, Fuquan & Kang, Yuanlei & Zhang, Jihui, 2021. "The effect of passenger attributes on alighting and boarding efficiency based on social force model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
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