Link prediction method for social networks based on a hierarchical and progressive user interaction matrix

Link prediction within social networks represents a notable research area. Leveraging semantic text and multidimensional user interaction features, this paper introduces a hierarchical and progressive user interaction matrix-based link prediction method in social networks. First, considering the dynamic nature of a user’s semantic text features, the word2vec algorithm was employed to extract text features. Given the long short-term memory algorithm’s proficiency in learning long-term sequence dependencies, we proposed a method to represent time-series text called “TT2vec” in order to capture the evolution pattern of a user’s semantic text features. Second, to address the multidimensionality of user interactions, we constructed a hierarchical and progressive interaction network adjacency matrix, thereby deriving advanced user interaction features using an enhanced graph convolutional network. Then, using TT2vec to further capture the interaction network’s evolution, we proposed HPIN2vec, which is a method for representing hierarchical and progressive user interaction networks. Finally, for the users’ semantic text features and multidimensional interaction features, we proposed a link prediction method based on graph attention networks (GATs) and users’ dynamic features, considering the advantage of GAT aggregating neighbor node features. Experimental findings indicated that the proposed method not only identifies immediate dynamics in user features but also enhances link prediction accuracy through complex hierarchical and progressive interaction patterns.