ICML 2025

Aggregation Buffer: Revisiting DropEdge with a New Parameter Block
Dooho Lee 1 Myeong Kong 1 Sagad Hamid 1 2 Cheonwoo Lee 1 Jaemin Yoo 1
Abstract
We revisit DropEdge, a data augmentation tech-
nique for GNNs which randomly removes edges
to expose diverse graph structures during train-
ing. While being a promising approach to ef-
fectively reduce overfitting on specific connec-
tions in the graph, we observe that its potential
performance gain in supervised learning tasks
is significantly limited. To understand why, we
provide a theoretical analysis showing that the
limited performance of DropEdge comes from
the fundamental limitation that exists in many
GNN architectures. Based on this analysis, we
propose Aggregation Buffer, a parameter block
specifically designed to improve the robustness of
GNNs by addressing the limitation of DropEdge.
Our method is compatible with any GNN model,
and shows consistent performance improvements
on multiple datasets. Moreover, our method ef-
fectively addresses well-known problems such as
degree bias or structural disparity as a unifying so-
lution. Code and datasets are available at https:
//github.com/dooho00/agg-buffer.
1. Introduction
Graph-structured data are pervasive across various research
fields and real-world applications, as graphs naturally cap-
ture essential relationships among entities in complex sys-
tems. Graph neural networks (GNNs) have emerged as a
powerful framework to effectively incorporate these rela-
tionships for graph-related tasks. In contrast to traditional
multi-layer perceptrons (MLPs), which solely consider node
features, GNNs additionally take advantage of edge informa-
tion to incorporate crucial interrelations between node fea-
tures (Kipf & Welling, 2017; Gasteiger et al., 2019; Hamil-
ton et al., 2017; Veliˇckovi´c et al., 2018). As a consequence,
1School of Electrical Engineering, KAIST, Daejeon, Re-
public of Korea 2Computer Science Department, University of
M¨unster, M¨unster, Germany. Corresp...