论文标题
时间链接预测：统一框架，分类学和审查
Temporal Link Prediction: A Unified Framework, Taxonomy, and Review
论文作者
论文摘要
动态图是对各种复杂系统（例如社交网络和通信网络）的进化行为的通用抽象和描述。时间链接预测（TLP）是动态图上的经典但充满挑战的推理任务，该任务可预测基于历史拓扑的未来链接。预测的未来拓扑可用于支持现实世界系统（例如资源预分配）上的一些高级应用程序，以提供更好的系统性能。这项调查对现有TLP方法进行了全面审查。具体而言，我们首先给出了有关数据模型，任务设置和学习范式的正式问题陈述和初步，这些范例通常在相关研究中使用。进一步引入了分层细粒分类法，以根据其数据模型，学习范式和技术对现有方法进行分类。从一般的角度来看，我们提出了一个统一的编码框架框架来制定所有审查的方法，其中不同的方法仅在框架的某些组件方面有所不同。此外，我们设想使用开源项目OPENTLP为社区提供服务，该项目使用拟议的统一框架重构或实现了一些代表性的TLP方法，并总结了其他公共资源。总而言之，我们终于讨论了最近的研究中的高级主题，并突出了可能的未来方向。
Dynamic graphs serve as a generic abstraction and description of the evolutionary behaviors of various complex systems (e.g., social networks and communication networks). Temporal link prediction (TLP) is a classic yet challenging inference task on dynamic graphs, which predicts possible future linkage based on historical topology. The predicted future topology can be used to support some advanced applications on real-world systems (e.g., resource pre-allocation) for better system performance. This survey provides a comprehensive review of existing TLP methods. Concretely, we first give the formal problem statements and preliminaries regarding data models, task settings, and learning paradigms that are commonly used in related research. A hierarchical fine-grained taxonomy is further introduced to categorize existing methods in terms of their data models, learning paradigms, and techniques. From a generic perspective, we propose a unified encoder-decoder framework to formulate all the methods reviewed, where different approaches only differ in terms of some components of the framework. Moreover, we envision serving the community with an open-source project OpenTLP that refactors or implements some representative TLP methods using the proposed unified framework and summarizes other public resources. As a conclusion, we finally discuss advanced topics in recent research and highlight possible future directions.
