Knowledge services are becoming a rising star in the family of XaaS (Everything as a Service). In recent years, people are more willing to search for answers and share their knowledge directly over the Internet, which drives the knowledge service ecosystem prosperous and quickly evolve. In this article, we aim to predict the popularity of knowledge services, which will benefit the downstream industries that provide Knowledge as a Service (KaaS). Toward such a task, the spatial interactions (e.g., hyperlinks in Wikipedia) and temporal observations (e.g., page views) provide crucial information. However, it is difficult to utilize this information due to: (i) complicated and different usage observations, (ii) intricate and evolutionary spatial interactions, and (iii) small world trait of the network. To tackle such issues, we propose Evolutionary Graph Convolutional Recurrent Neural Networks (E-GCRNNs) to simultaneously model both temporal and spatial dependencies of knowledge services from their evolving networks. Specifically, an elementary unit (called E-GCGRU) is designed to dynamically perceive the evolutionary spatial dependencies, aggregate spatial information of knowledge services, and model the temporal patterns by considering the records of one sequence and its neighbors simultaneously. Additionally, a localized mini-batch training scheme is developed, which allows the E-GCRNNs to work on large-scale knowledge services networks and reduce the prediction bias caused by the small world trait. Extensive experiments on real-world datasets have demonstrated that the proposed E-GCRNNs outperform baselines in terms of prediction accuracy, especially with the prediction range being longer, while remaining computationally efficient.

中文翻译：

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迈向知识即服务 (KaaS)：利用图神经网络预测知识服务的流行度


知识服务正在成为XaaS（一切即服务）家族中的后起之秀。近年来，人们更愿意直接通过互联网寻找答案、分享知识，带动了知识服务生态系统的繁荣和快速演化。在本文中，我们的目标是预测知识服务的普及程度，这将使提供知识即服务（KaaS）的下游行业受益。对于这样的任务，空间交互（例如，维基百科中的超链接）和时间观察（例如，页面视图）提供了关键信息。然而，由于以下原因，利用这些信息很困难：（i）复杂且不同的使用观察，（ii）复杂且进化的空间交互，以及（iii）网络的小世界特征。为了解决这些问题，我们提出了进化图卷积循环神经网络（E-GCRNN），以同时对知识服务从其进化网络中的时间和空间依赖性进行建模。具体来说，设计了一个基本单元（称为 E-GCGRU）来动态感知进化空间依赖性，聚合知识服务的空间信息，并通过同时考虑一个序列及其邻居的记录来对时间模式进行建模。此外，还开发了本地化的小批量训练方案，使 E-GCRNN 能够在大规模知识服务网络上工作，并减少小世界特征造成的预测偏差。对现实数据集的大量实验表明，所提出的 E-GCRNN 在预测精度方面优于基线，特别是在预测范围更长的情况下，同时保持计算效率。