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<p>Temporal networks offer a powerful lens for understanding systems where relationships evolve, ranging from human interactions and communication patterns to transport and biological processes. As research in this area expands, so does the need for a structured and application-driven review. In this paper, we go beyond traditional surveys by introducing a fresh organisational perspective grounded in operations rather than only models or applications. We first examine major representation techniques, categorised by temporal granularity and the level of structural detail preserved. Building on this, we propose a novel taxonomy that groups temporal network operations into five core classes: analytical, topological, traversal-based, NP-complete, and data-handling. This classification bridges algorithmic methods with practical needs, helping researchers navigate the space more effectively. For each class, we highlight key operations and demonstrate their impact. By linking how temporal networks are represented to how they are analysed, this review offers a clear and unified guide for selecting the right tools and advancing the field in both theory and practice.</p>

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A Review of Temporal Networks: Operations and Applications

  • 期刊论文

  • 发表日期：

    2025年7月23日

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  • Aaqib Zahoor,
  • Iqra Altaf Gillani,
  • Janib ul Bashir

摘要

Temporal networks offer a powerful lens for understanding systems where relationships evolve, ranging from human interactions and communication patterns to transport and biological processes. As research in this area expands, so does the need for a structured and application-driven review. In this paper, we go beyond traditional surveys by introducing a fresh organisational perspective grounded in operations rather than only models or applications. We first examine major representation techniques, categorised by temporal granularity and the level of structural detail preserved. Building on this, we propose a novel taxonomy that groups temporal network operations into five core classes: analytical, topological, traversal-based, NP-complete, and data-handling. This classification bridges algorithmic methods with practical needs, helping researchers navigate the space more effectively. For each class, we highlight key operations and demonstrate their impact. By linking how temporal networks are represented to how they are analysed, this review offers a clear and unified guide for selecting the right tools and advancing the field in both theory and practice.