LazyGNN：通过惰性传播实现大规模图神经网络
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
AI总结：
本文提出 LazyGNN，用浅层模型捕获长距离依赖，避免深层 GNN 的邻域爆炸问题，并支持小批量采样加速，在大规模图基准上取得优越性能。
摘要：
最近的研究表明，通过更深的图神经网络（GNN）可以捕获图中的长距离依赖关系。但更深的 GNN 由于大规模图中的邻域爆炸问题而面临长期的扩展性挑战。在这项工作中，我们提出用较浅的模型而非更深的模型来捕获图中的长距离依赖关系，从而得到一个更高效的图表示学习模型 LazyGNN。此外，我们通过开发小批量 LazyGNN，证明了 LazyGNN 与现有的可扩展方法（如采样方法）兼容，可进一步加速。综合实验证明了其在大规模基准上具有优越的预测性能和扩展性。LazyGNN 的实现可在 https://github.com/RXPHD/Lazy_GNN 获取。
英文摘要：
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting scalability challenge due to the neighborhood explosion problem in large-scale graphs. In this work, we propose to capture long-distance dependency in graphs by shallower models instead of deeper models, which leads to a much more efficient model, LazyGNN, for graph representation learning. Moreover, we demonstrate that LazyGNN is compatible with existing scalable approaches (such as sampling methods) for further accelerations through the development of mini-batch LazyGNN. Comprehensive experiments demonstrate its superior prediction performance and scalability on large-scale benchmarks. The implementation of LazyGNN is available at https://github.com/RXPHD/Lazy_GNN.
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。
