Implicit link prediction based on extended social graph

Abstract

Link prediction infers the likelihood of a connection between two nodes based on network structural information, aiming to foresee potential latent relationships within the network. In social networks, nodes typically represent users, and links denote the relationships between users. However, some user nodes in social networks are hidden due to unknown or incomplete link information. The prediction of implicit links between these nodes and other user nodes is hampered by incomplete network structures and partial node information, affecting the accuracy of link prediction. To address these issues, this paper introduces an implicit link prediction algorithm based on extended social graph (ILP-ESG). The algorithm completes user attribute information through a multi-task fusion attribute inference framework built on associative learning. Subsequently, an extended social graph is constructed based on user attribute relations, social relations, and discourse interaction relations, enriching user nodes with comprehensive representational information. A semi-supervised graph autoencoder is then employed to extract features from the three types of relationships in the extended social graph, obtaining feature vectors that effectively represent the multidimensional relationship information of users. This facilitates the inference of potential implicit links between nodes and the prediction of hidden user relationships with others. This algorithm is validated on real datasets, and the results show that under the Facebook dataset, the algorithm improves the AUC and Precision metrics by an average of 5.17\(\%\) and 9.25\(\%\) compared to the baseline method, and under the Instagram dataset, it improves by 7.71\(\%\) and 16.16\(\%\), respectively. Good stability and robustness are exhibited, ensuring the accuracy of link prediction.