A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability

TL;DR

This paper presents a comprehensive benchmark evaluating 17 graph pooling methods across 28 datasets, focusing on effectiveness, robustness, and generalizability in various graph learning tasks.

Contribution

It introduces a standardized benchmark with extensive experiments, including robustness and efficiency analyses, to fairly compare graph pooling approaches.

Findings

Graph pooling methods show varying effectiveness across tasks.

Robustness analysis reveals vulnerability to noise attacks.

Benchmark provides valuable insights for future graph pooling research.

Abstract

Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized experimental settings and fair benchmarks to evaluate their performance. To address this issue, we have constructed a comprehensive benchmark that includes 17 graph pooling methods and 28 different graph datasets. This benchmark systematically assesses the performance of graph pooling methods in three dimensions, i.e., effectiveness, robustness, and generalizability. We first evaluate the performance of these graph pooling approaches across different tasks including graph classification, graph regression and node classification. Then, we investigate their performance under potential noise attacks and out-of-distribution shifts in real-world scenarios. We also involve detailed efficiency analysis, backbone analysis, parameter analysis and visualization to provide more evidence. Extensive experiments validate the strong capability and applicability of graph pooling approaches in various scenarios, which can provide valuable insights and guidance for deep geometric learning research. The source code of our benchmark is available at https://github.com/goose315/Graph_Pooling_Benchmark.