Bodnar Cristian, Cangea Cătălina, Liò Pietro
Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.
Front Big Data. 2021 Jun 16;4:680535. doi: 10.3389/fdata.2021.680535. eCollection 2021.
Graph summarization has received much attention lately, with various works tackling the challenge of defining pooling operators on data regions with arbitrary structures. These contrast the grid-like ones encountered in image inputs, where techniques such as max-pooling have been enough to show empirical success. In this work, we merge the Mapper algorithm with the expressive power of graph neural networks to produce topologically grounded graph summaries. We demonstrate the suitability of Mapper as a topological framework for graph pooling by proving that Mapper is a generalization of pooling methods based on soft cluster assignments. Building upon this, we show how easy it is to design novel pooling algorithms that obtain competitive results with other state-of-the-art methods. Additionally, we use our method to produce GNN-aided visualisations of attributed complex networks.
图摘要最近受到了广泛关注，各种工作都在应对在具有任意结构的数据区域上定义池化算子的挑战。这些与图像输入中遇到的网格状结构形成对比，在图像输入中，诸如最大池化之类的技术已足以显示出经验上的成功。在这项工作中，我们将Mapper算法与图神经网络的表达能力相结合，以生成基于拓扑的图摘要。我们通过证明Mapper是基于软聚类分配的池化方法的推广，证明了Mapper作为图池化的拓扑框架的适用性。在此基础上，我们展示了设计新颖的池化算法是多么容易，这些算法能与其他现有最先进方法取得有竞争力的结果。此外，我们使用我们的方法来生成属性复杂网络的GNN辅助可视化。
