DLMinTC+: A Deep Learning Based Algorithm for Minimum Timeline Cover on Temporal Graphs

Combinatorial optimization on temporal graphs is critical for summarizing dynamic networks in various fields, including transportation, social networks, and biology. Among these problems, the Minimum Timeline Cover (MinTCover) problem, aimed at identifying minimal activity intervals for representing temporal interactions, remains underexplored in the context of advanced machine learning techniques. Existing heuristic and approximate methods, while effective in certain scenarios, struggle with capturing complex temporal dependencies and scalability in dense, large-scale networks. Addressing this gap, this paper introduces DLMinTC+, a novel deep learning-based algorithm for solving the MinTCover problem. The proposed method integrates Graph Neural Networks for structural embedding, Transformer-based temporal encoding, and Pointer Networks for activity interval selection, coupled with an iterative adjustment algorithm to ensure valid solutions. Key contributions include (i) demonstrating the efficacy of deep learning for temporal combinatorial optimization, achieving superior accuracy and efficiency over state-of-the-art heuristics, and (ii) advancing the analysis of temporal knowledge graphs by incorporating robust, time-sensitive embeddings. Extensive evaluations on synthetic and real-world datasets highlight DLMinTC+'s ability to achieve significant coverage size reduction while maintaining generalization, offering a scalable and precise solution for complex temporal networks.