Neighbor2Seq：通过将邻居转换为序列对海量图进行深度学习
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
摘要：
现代图神经网络（GNNs）使用消息传递方案，并在许多领域取得了巨大的成功。然而，这种递归设计固有地导致过多的计算和内存需求，使其不适用于大规模的现实世界的图。在这项工作中，我们提出了Neighbor2Seq将每个节点的分层邻域转换为序列。这种新颖的转换使后续的小批量训练能够用于一般的深度学习操作，例如卷积和注意力，这些操作是为网格状数据设计的，并且在各个领域都表现出强大的功能。因此，我们的Neighbor2Seq通过预先计算Neighbor2Seq变换，自然赋予GNN在网格状数据上进行深度学习操作的效率和优势。我们在一个拥有超过1.11亿个节点和16亿条边的大型图以及几个中等规模的图上评估了我们的方法。结果表明，我们提出的方法是可扩展的大规模图，并实现了卓越的性能在大规模和中等规模的图。我们的代码可在https://github.com/divelab/Neighbor2Seq上获得。
英文摘要：
Modern graph neural networks (GNNs) use a message passing scheme and have achieved great success in many fields. However, this recursive design inherently leads to excessive computation and memory requirements, making it not applicable to massive real-world graphs. In this work, we propose the Neighbor2Seq to transform the hierarchical neighborhood of each node into a sequence. This novel transformation enables the subsequent mini-batch training for general deep learning operations, such as convolution and attention, that are designed for grid-like data and are shown to be powerful in various domains. Therefore, our Neighbor2Seq naturally endows GNNs with the efficiency and advantages of deep learning operations on grid-like data by precomputing the Neighbor2Seq transformations. We evaluate our method on a massive graph, with more than 111 million nodes and 1.6 billion edges, as well as several medium-scale graphs. Results show that our proposed method is scalable to massive graphs and achieves superior performance across massive and medium-scale graphs. Our code is available at https://github.com/divelab/Neighbor2Seq.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
