Research on complex network link prediction algorithm based on network structure

Yabing Yao; Zeguang Liu

doi:10.1117/12.2671721

1 June 2023 Research on complex network link prediction algorithm based on network structure

Yabing Yao, Zeguang Liu

Proceedings Volume 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022); 1262539 (2023) https://doi.org/10.1117/12.2671721
Event: International Conference on Mathematics, Modeling and Computer Science (MMCS2022),, 2022, Wuhan, China

Abstract

The complex system in real life is regarded as the network structure, then the individuals in the system are the nodes in the network, and the relationship between individuals can be expressed by connecting edges. The complex network will change with time, and the information contained in it will change constantly. The edges in the network are the effective carrier of information interaction between individuals, so it is very important to dig deep and analyze them effectively. As an important means of mining network connection information, link prediction can not only master more hidden information from it, but also discover missing information in the research. It is a common way to complete the incomplete network. In the process of being included in the prediction technology research, researchers have put forward a variety of application algorithms, among which the most representative is the link prediction method based on similarity. Therefore, on the basis of determining the link prediction and technology research status of complex network, this paper puts forward a new method to calculate node weight and path weight, and combines them together for experimental analysis. The final results show that the algorithm is more accurate and effective in practice.

Citation Download Citation

Yabing Yao and Zeguang Liu "Research on complex network link prediction algorithm based on network structure", Proc. SPIE 12625, International Conference on Mathematics, Modeling, and Computer Science (MMCS2022), 1262539 (1 June 2023); https://doi.org/10.1117/12.2671721

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