# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
###### Abstract  
For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security. H +
Footnote †: journal: Applied sciences  
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as a key issue in complex network research, utilizes observable network structures to explore the basic principles of network formation, predict possible future links, or identify missing links in the network. This not only helps to understand the evolution and stability of networks deeply but also provides important ideas for solving practical problems in reality. Port systems can be described as complex networks [7; 8; 9], where nodes represent entities and interactions between entities can be described as edges or links. Link prediction in networks refers to how to predict the possibility of generating a connection between two nodes in a network that have not yet generated a connected edge by using known information about the network structure and other information, which helps to excavate the network structure and evolution. With the wide application of complex networks in modern society, link prediction has become an important network analysis technique with far-reaching practical applications. Intending to make the link prediction model better adapted to different types of complex networks and improve the prediction ability of potential connections in the network, the study of link prediction with the help of graph neural networks combined with higher-order topology information provides a more in-depth and effective method for understanding and applying complex networks. Ensuring accurate acquisition of node information in link prediction is a challenging task, especially when relying solely on the structural information of the network. Taking the port system network applied in this article as an example, considering the possible privacy protection measures, direct access to certain key information is restricted, which increases the complexity and challenge of information acquisition. Traditional hypergraph models often overly rely on node attribute information and overlook the importance of network topology. Therefore, it is urgent to further explore how to effectively integrate network structures and attribute information to improve the accuracy and robustness of link prediction models. Although the use of complex networks in link prediction can better apply technology to real life, and although the study of high-order topologies can improve the accuracy and robustness of link prediction, there are still some problems: 1. In some current practical applications, the relationship between nodes can be extremely complex, and the edges of the general graph can only be connected to two nodes. It is difficult to accurately represent many-to-many, many-to-one and other complex relationships; thus, the general graph can not meet the needs; 2.