论文标题
用于监督动态图学习的编码器 - 编码器架构：调查
Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey
论文作者
论文摘要
近年来，普遍的在线服务会产生大量的用户活动数据。服务提供商收集这些数据以执行客户行为分析，并提供更好，更自定义的服务。这些数据中的大多数可以被建模和存储为图形，例如YouTube中的用户video交互图中的社交图。这些图需要随着时间的流逝而发展，以捕获现实世界中的动态，从而导致动态图的发明。但是，嵌入在动态图中的时间信息为分析和部署它们带来了新的挑战。事件的稳定性，时间信息学习和明确的时间维度是动态图学习中的一些示例挑战。为了提出对行业和学术界的便利参考，这项调查介绍了基于动态图演化理论的三个阶段，以重复的时间学习框架，以解释使用通用框架的时间信息的学习。在此框架下，该调查类别并审查了不同的可学习的编码器架构，以进行监督的动态图学习。我们认为，这项调查可以为研究人员和工程师提供有用的准则，以寻找适合其动态学习任务的合适图形结构。
In recent years, the prevalent online services generate a sheer volume of user activity data. Service providers collect these data in order to perform client behavior analysis, and offer better and more customized services. Majority of these data can be modeled and stored as graph, such as the social graph in Facebook, user-video interaction graph in Youtube. These graphs need to evolve over time to capture the dynamics in the real world, leading to the invention of dynamic graphs. However, the temporal information embedded in the dynamic graphs brings new challenges in analyzing and deploying them. Events staleness, temporal information learning and explicit time dimension usage are some example challenges in dynamic graph learning. In order to offer a convenient reference to both the industry and academia, this survey presents the Three Stages Recurrent Temporal Learning Framework based on dynamic graph evolution theories, so as to interpret the learning of temporal information with a generalized framework. Under this framework, this survey categories and reviews different learnable encoder-decoder architectures for supervised dynamic graph learning. We believe that this survey could supply useful guidelines to researchers and engineers in finding suitable graph structures for their dynamic learning tasks.
