Graph pooling methods provide mechanisms for structure reduction that are intended to ease the diffusion of context between nodes further in the graph, and that typically leverage community discovery mechanisms or node and edge pruning heuristics. In this paper, we introduce a novel pooling technique which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns. Our pooling method, named KPlexPool, builds on the concepts of graph covers and k-plexes, i.e. pseudo-cliques where each node can miss up to k links. The experimental evaluation on benchmarks on molecular and social graph classification shows that KPlexPool achieves state of the art performances against both parametric and non-parametric pooling methods in the literature, despite generating pooled graphs based solely on topological information.

图池方法提供了结构简化机制，旨在缓解图中节点之间上下文的进一步扩散，并且通常利用社区发现机制或节点和边缘修剪启发式方法。在本文中，我们介绍了一种新的池化技术，它借鉴了图论中的经典结果，它是非参数的，并且可以很好地推广到不同性质和连接模式的图。我们的池化方法，名为KPlexPool，建立在图覆盖和k -plex的概念之上，即每个节点可以错过多达k 个链接的伪集团。对分子和社会图分类基准的实验评估表明，KPlexPool 尽管仅基于拓扑信息生成池化图，但在文献中针对参数化和非参数化池化方法实现了最先进的性能。