论文标题
通过知识蒸馏进行时空预测的子图表学习
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation
论文作者
论文摘要
研究大图中相互作用的挑战之一是学习它们的多样化模式和各种相互作用类型。因此，仅考虑一个分布和模型来研究所有节点，而忽略了他们在社区中的多样性和本地特征，可能会严重影响整体性能。基于图中节点的结构信息及其之间的相互作用，主图可以分为多个子图。该图分配可能会极大地影响学习过程，但是总体性能高度取决于聚类方法，以避免误导模型。在这项工作中，我们提出了一个名为KD-SGL的新框架，以有效地学习子图，我们在其中定义了一个全局模型，以了解每个子图形的图形和多个局部模型的整体结构。我们评估提出的框架的性能，并在公共数据集上进行评估。基于所达到的结果，它可以提高最先进的时空模型的性能，其结果与复杂性较小的模型集合相比具有可比的结果。
One of the challenges in studying the interactions in large graphs is to learn their diverse pattern and various interaction types. Hence, considering only one distribution and model to study all nodes and ignoring their diversity and local features in their neighborhoods, might severely affect the overall performance. Based on the structural information of the nodes in the graph and the interactions between them, the main graph can be divided into multiple sub-graphs. This graph partitioning can tremendously affect the learning process, however the overall performance is highly dependent on the clustering method to avoid misleading the model. In this work, we present a new framework called KD-SGL to effectively learn the sub-graphs, where we define one global model to learn the overall structure of the graph and multiple local models for each sub-graph. We assess the performance of the proposed framework and evaluate it on public datasets. Based on the achieved results, it can improve the performance of the state-of-the-arts spatiotemporal models with comparable results compared to ensemble of models with less complexity.
