A unified temporal link prediction framework based on nonnegative matrix factorization and graph regularization

Complex real-world systems, evolving over time, can be modeled as dynamic networks. Numerous studies have focused on utilizing information about the entities and relationships within networks. Temporal link prediction, a challenging yet critical task for dynamic networks, aims to forecast the appearance and disappearance of links in future snapshots based on the network structure observed in previous snapshots. However, existing works have not fully utilized information from historical networks, such as evolving structures and community data. Additionally, nonnegative matrix factorization (NMF) techniques are unable to automatically extract nonlinear spatial and temporal features from dynamic networks. In this paper, we introduce a unified temporal link prediction framework, EDeepEye, which leverages NMF and graph regularization to predict temporal links. Based on this framework, we propose three novel methods: SDeepEye, GDeepEye, and QDeepEye, which incorporate prior information, weighted matrices, and modularity matrices, respectively. Additionally, we provide effective multiplicative updating rules for the factors of the methods, which learn latent features from the temporal topological structure. Three evaluation metrics, i.e., area under the receiver operator characteristic curve, Precision and root mean squared error, are applied to verify the superiority of the proposed methods. The results of empirical study show that our proposed methods outperform the baseline methods on eight real-world networks and 16 synthetic networks.