系统梳理时序网络链接预测方法，区分表示与推理机制。
A Survey of Link Prediction in Temporal Networks
- 提出新分类框架，区分表示学习与推理方法
- 分析不同表示技术对动态结构的捕捉能力
- 适合研究时序网络建模与可解释性的人士
过去十年，时序网络因其在复杂系统动态交互建模中的重要性而备受关注。该领域核心挑战之一是时序链接预测（TLP），旨在通过分析历史网络结构预测未来连接，广泛应用于社交网络分析等领域。尽管已有综述探讨了TLP的部分方面，但普遍缺乏区分表示与推理方法的综合框架。本文填补这一空白，提出新颖分类法，明确考察现有方法中的表示与推理过程，系统分类TLP方法。我们分析不同表示技术如何捕捉时间与结构动态，评估其与各类推理方法在归纳与直推任务中的兼容性。该分类不仅厘清方法格局，还揭示了现有技术的潜在组合。该框架为应对新兴挑战如模型可解释性与复杂时序网络的可扩展架构提供了系统基础。
原文摘要 · Abstract (English)
Temporal networks have gained significant prominence in the past decade for modelling dynamic interactions within complex systems. A key challenge in this domain is Temporal Link Prediction (TLP), which aims to forecast future connections by analysing historical network structures across various applications including social network analysis. While existing surveys have addressed specific aspects of TLP, they typically lack a comprehensive framework that distinguishes between representation and inference methods. This survey bridges this gap by introducing a novel taxonomy that explicitly examines representation and inference from existing methods, providing a novel classification of approaches for TLP. We analyse how different representation techniques capture temporal and structural dynamics, examining their compatibility with various inference methods for both transductive and inductive prediction tasks. Our taxonomy not only clarifies the methodological landscape but also reveals promising unexplored combinations of existing techniques. This taxonomy provides a systematic foundation for emerging challenges in TLP, including model explainability and scalable architectures for complex temporal networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。