论文标题
增强图
Graph Augmentation Learning
论文作者
论文摘要
图表增强学习（GAL）为处理不完整的数据，噪声数据等提供了出色的解决方案。为基于图的应用程序（例如社交网络分析和交通流预测）提出了许多GAL方法。但是，这些GAL方法有效性的根本原因尚不清楚。结果，如何为某个应用程序方案选择最佳的图表增强策略仍在黑匣子中。对于学者来说，缺乏系统的，全面和实验验证的GAL指南。因此，在这项调查中，我们从宏（图），中索（Graph），Meso（subgraph）和Micro（节点/边缘）级别进行深入审查GAL技术。我们进一步详细说明了GAL如何增强数据质量和模型性能。还通过不同的应用程序场景，即特定于数据，特定于模型和混合动力方案来讨论增强策略和图形学习模型的聚合机制。为了更好地显示GAL的表现，我们通过实验验证了不同下游任务中不同GAL策略的有效性和适应性。最后，我们分享了关于GAL的几个开放问题的见解，包括异质性，时空动力学，可伸缩性和概括。
Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for graph-based applications such as social network analysis and traffic flow forecasting. However, the underlying reasons for the effectiveness of these GAL methods are still unclear. As a consequence, how to choose optimal graph augmentation strategy for a certain application scenario is still in black box. There is a lack of systematic, comprehensive, and experimentally validated guideline of GAL for scholars. Therefore, in this survey, we in-depth review GAL techniques from macro (graph), meso (subgraph), and micro (node/edge) levels. We further detailedly illustrate how GAL enhance the data quality and the model performance. The aggregation mechanism of augmentation strategies and graph learning models are also discussed by different application scenarios, i.e., data-specific, model-specific, and hybrid scenarios. To better show the outperformance of GAL, we experimentally validate the effectiveness and adaptability of different GAL strategies in different downstream tasks. Finally, we share our insights on several open issues of GAL, including heterogeneity, spatio-temporal dynamics, scalability, and generalization.
