Distributed Processing and Management of Graph Neural Networks

对大量图形结构数据的分析、训练和推断的需求不断增加。像社交网络、通信网络和生物网络这样的图往往会变得非常庞大，并且需要大量的内存和计算能力来运行。这些要求很难通过单个专用机器来满足。除此之外，现在越来越多的系统变得对延迟至关重要。随着实时推荐系统的普及，我们希望在几毫秒内对传入的数据进行推断，随着数据量的增长，这将成为一个挑战。在过去的几年里，图神经网络一直是对图进行推理的主要框架。然而，现有的研究大多集中在静态图，可以适应到一个单一的机器。此外，这些模型是在整个图数据集上训练的，这对于延迟关键系统来说变得难以处理。在构建具有足够低延迟和高吞吐量的可扩展图神经网络方面存在一些挑战。由于给定的限制，在学术研究和大规模利用所提出的图神经网络之间存在技术差距。因此，该项目旨在为图神经网络开发一个可扩展的统一框架，以低延迟处理大规模不断变化的数据。特别是，我们计划1。探索将演化图数据划分到机器集群的方法。2.开发新的方法，对传入的数据进行增量学习。3.为分布式训练和图神经网络推理提供统一的框架。与EPSRC研究主题保持一致：人工智能和机器人，工程，人工智能和机器人，信息和通信技术（ICT）。

There has been an ever-increasing need for analysing, training and inferring over massive volumes of graph-structured data. Graphs like social interaction networks, communication networks, and biological networks tend to get quite massive in size and require lots of memory and computational power to operate. Such requirements are hardly met by a single dedicated machine. In addition to that, more and more systems, nowadays, are becoming latency-critical. As real-time recommendation systems prevail, we would like to infer over incoming data in a matter of milliseconds which becomes a challenge as the amount of data grows. Over the past years, Graph Neural Networks have been the predominant framework for making inferences over graphs. However, existing research mostly focuses on static graphs which can be fit into a single machine. Moreover, these models are trained over entire graph datasets, which become intractable for latency-critical systems. There are several challenges in building scalable Graph Neural Networks with sufficiently low latency and high throughput. There is a technological gap between the academic research and large-scale utilization of proposed Graph Neural Networks due to the given constraints. Hence, this project aims to develop a scalable and unified framework for Graph Neural Networks to handle large-scale evolving data with low latency. In particular, we plan to 1. Explore approaches for partitioning evolving graph data to a cluster of machines. 2. Develop new methods for incremental learning over the incoming data. 3. Provide a unified framework for distributed training and inferring with Graph Neural Networks. Alignment with EPSRC research themes: Artificial intelligence and robotics, Engineering, Artificial intelligence and robotics, Information and communication technologies (ICT).