# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 2 Related Work
Graph neural networks have been adopted by many scholars for link prediction due to their advantages of effectively capturing topology information, fusing node attributes, end-to-end learning, applicability to networks of different sizes and effective modeling of contextual information. However, most of the current link prediction methods mainly focus on low-order networks, ignoring the expression of complete semantic information between multiple entities, and, thus, there is a need to identify high-order topological structure information to more adequately capture the topological nature and complex associations of relational data. [PERSON] [17] proposed a higher-order link prediction algorithm based on higher-order structural information around nodes, which uses biased random wandering to abstract the higher-order structural information around nodes and maps the wandering sequences of the nodes into low-dimensional feature vectors using a representation learning approach to capture the higher-order structural features around nodes. To cope with the effect of network disconnection, an algorithm based on edge relation matrix decomposition is proposed to combine non-negative matrix decomposition with higher-order link prediction to capture the similarity between nodes and edges in order to synthesize the likelihood of generating higher-order structures between nodes and edges in the network. [PERSON] et al. [18] proposed an efficient primitive neighbor matrix learning algorithm for extracting higher-order structures in directed networks and constructing multiple primitive neighbor matrices, as well as to study a new encoder scheme consisting of three modules: node embedding, information fusion and link prediction. [PERSON] et al. [19] proposed a link prediction model based on the Weisfeiler-Lehman simplex neural network (WL-SNN), which represents higher-order topological information by simplex features and uses the WL algorithm to construct the local neighborhood to extract higher-order information, in which a third-order Laplace operator is set to capture the simplex features. In addition, a two-channel neural network structure consisting of a simplex neural network and graph convolutional neural network is constructed. [PERSON] et al. [20] proposed a heterogeneous hypergraph neural network (NOH) for sensing neighborhood overlap, which learns higher-order semantics while maintaining the original topology. It adaptively combines a heterogeneous hypergraph variational self-encoder with a neighborhood-overlapping perceptual graph neural network to take into account the heterogeneity of the graphs and learn the structural information of the graphs and estimate the overlapping neighborhoods for link prediction. [PERSON] et al. [21] proposed a knowledge graph embedding method called KANE, inspired by graph convolutional networks, to express high-order structural relationships between entities. [PERSON] [22] designed a prediction method with strong universality by making full use of the edge connection principle of a heterogeneous, attribute and time-series supernetwork. Aiming at the agglomeration characteristics of different links in heterogeneous supernetworks, a supernetwork link prediction model based on a hypergraph clustering parser is proposed. [PERSON] et al. [23] proposed two methods for link prediction, H-CCNC and FH-CCNC. These two methods combine node centrality and high-order clustering coefficients to predict missing links in the network from both global and local perspectives, demonstrating the potential to significantly improve prediction accuracy on multiple real datasets. These proposed high-order link prediction algorithms fully consider the high-order structural information around the nodes in the network and use different methods to capture and utilize this information to improve the accuracy and robustness of link prediction.