Explainable Multihop Social Link Prediction Based on Temporal Logical Rules in Dynamic Social Networks

The social link prediction poses a fundamental challenge in social network analysis, aiming to forecast missing interactions among users. Given the dynamic evolution mechanism of social networks, prevailing efforts have introduced embedding-based approaches to address temporal link prediction in dynamic social networks. However, these approaches often struggle to handle explainability, multirelations, and multihop relation prediction simultaneously. To overcome these limitations, we present an innovative multihop temporal social link prediction model based on temporal logic embedding (TLE), which leverages temporal knowledge graphs and logic rules. First, inspired by the temporal knowledge graph, we construct temporal social knowledge graphs (TSKGs) to model dynamic social networks. Then, we incorporate the orthogonal transformation matrix into the graph neural networks (GNNs), thereby facilitating the learning of time-aware relation representations. Furthermore, we define temporal social random walks from the TSKG to generate temporal social rules. Subsequently, TLE employs time-aware relation embedding to calculate the confidence associated with each rule. Finally, TLE combines the confidence score and time difference to obtain the final link prediction, providing explainability for multihop link predictions. The experiments carried out on four datasets indicate the superiority of TLE in the social link prediction within dynamic social networks.