Abstract 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.

Davide Bacciu

Alessio Conte

Roberto Grossi

Francesco Landolfi

Andrea Marino

Data Mining and Knowledge Discovery

数据收集、存储和分发方面的进步已经产生了对帮助数据分析的计算工具和技术的需求。数据库中的数据挖掘和知识发现 (KDD) 是一个快速发展的研究和应用领域，它建立在许多领域的技术和理论之上，包括统计学、数据库、模式识别和学习、数据可视化、不确定性建模、数据仓库和 OLAP，优化和高性能计算。

With advancements in data collection, storage, and distribution technologies, there is a growing demand for tools and techniques that assist in data analysis. Data Mining and Knowledge Discovery (KDD) is a rapidly evolving field of research and application that builds upon technologies and theories from various disciplines, including statistics, databases, pattern recognition and learning, data visualization, uncertainty modeling, data warehousing and OLAP, optimization, and high-performance computing.

随着数据收集、存储和分发技术的进步，市场对数据分析工具和技术的需求日益增长。数据挖掘与知识发现（Data Mining and Knowledge Discovery，KDD）是一个快速发展的研究与应用领域，融合了统计学、数据库、模式识别与学习、数据可视化、不确定性建模、数据仓库与OLAP、优化以及高性能计算等多个学科的技术和理论。

K-plex cover pooling for graph neural networks

A novel pooling technique is introduced which borrows from classical results in graph theory that is non-parametric and generalizes well to graphs of different nature and connectivity patterns, despite generating pooled graphs based solely on topological information.

Category	Quartile
计算机科学	3
计算机科学, 计算机人工智能	3
计算机科学, 计算机信息系统	3

K-plex cover pooling for graph neural networks

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Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	2