# A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm
## 2 Related Work  
In response to increasingly complex social and information networks, researchers have proposed numerous link prediction methods in order to comprehensively understand and utilize these intricate networks. [PERSON] et al. [10] proposed a key node recognition algorithm, KNIA-MWF, based on multi-attribute weighted fusion. This algorithm combines the objective entropy weight method (EWM) and subjective analytic hierarchy process (AHP) to quantitatively analyze the impact of different attributes on node importance and to comprehensively evaluate the structural importance of nodes in the network through the final topological importance (FTI). This provided important ideas for link prediction in subsequent research. [PERSON] et al. [11] proposed an integrated model-based link prediction algorithm (EMLP) based on the idea of the integrated model, combined with logistic regression and superposition techniques, to improve the stability and accuracy of link prediction, and they experimentally proved that the link prediction of this method has better stability and accuracy. [PERSON] [12] proposed a single-mechanism link prediction algorithm based on node mechanism similarity, a hybrid link prediction algorithm based on an information entropy improved principal component analysis model and a hybrid link prediction algorithm based on ensemble learning. The reliability and stability of the prediction accuracy of the proposed three algorithms in the network were obtained by experimental tests. [PERSON] et al. [13] proposed a framework based on extensible and universal graph neural networks. The model learns the critical scores of nodes/links on a small representative subset of nodes/links and predicts large graphs by small graphs. The critical scores of nodes/links in large graphs are used to prove the scalability and accuracy of the framework. [PERSON] et al. [14] proposed an edge-convolution-based graph neural network called EdgeConvNorm for link prediction. The Hadamard product of the final learned link representation is to determine the link between two nodes via a binary classifier sigmoid. Intending to solve the over-smoothing problem of the model, a normalization strategy for the link representation is proposed. [PERSON] et al. [15] proposed an extended network self-representation (ENSR) model based on the absence of predefined assumptions about the mechanism of network link formation, which produces an optimization model of the optimal ENSR function by minimizing the Frobenius paradigm between the network adjacency matrix and its ENSR transform. Moreover, the ENSR model performs well in link prediction and can adaptively discover potential link formation mechanisms in complex networks. [PERSON] [16] proposed a link prediction model called DME based on neighbor contribution and path propagation for the intra-layer link prediction of single-layer networks. Aiming at the inter-layer link prediction of a two-layer network, a link prediction model called SWLEV based on the weighted embedding of edges in the network layer is proposed. By combining edge betweenness and degree centrality, an edge-weighted model is proposed, and an appropriate embedding method is added. The network structure is captured by combining the first-order proximity of the edge strength and the second-order proximity of the number of common neighbors to reduce the dimension of the network. These approaches demonstrate rich exploration and innovation in link prediction for complex networks. From integrated models to graph neural networks, as well as models based on mechanisms without assumptions, all of them meet the experimental expectations. In intricate networks, the in-depth understanding of higher-order structural information is of great significance in solving practical problems. With the wide application of complex networks, link prediction has become an important task.