Effects of Dropout on Performance in Long-range Graph Learning Tasks

TL;DR

This paper investigates the impact of Dropout techniques on long-range graph learning tasks, revealing limitations of existing methods and proposing DropSens to better preserve long-range information in deep GNNs.

Contribution

The paper introduces DropSens, a sensitivity-aware dropout method that improves long-range interaction modeling in deep GNNs, and provides theoretical and empirical analysis of existing dropout algorithms.

Findings

DropEdge variants reduce sensitivity between distant nodes.

Existing dropout methods are unsuitable for long-range tasks.

DropSens outperforms graph rewiring techniques in experiments.

Abstract

Message Passing Neural Networks (MPNNs) are a class of Graph Neural Networks (GNNs) that propagate information across the graph via local neighborhoods. The scheme gives rise to two key challenges: over-smoothing and over-squashing. While several Dropout-style algorithms, such as DropEdge and DropMessage, have successfully addressed over-smoothing, their impact on over-squashing remains largely unexplored. This represents a critical gap in the literature, as failure to mitigate over-squashing would make these methods unsuitable for long-range tasks -- the intended use case of deep MPNNs. In this work, we study the aforementioned algorithms, and closely related edge-dropping algorithms -- DropNode, DropAgg and DropGNN -- in the context of over-squashing. We present theoretical results showing that DropEdge-variants reduce sensitivity between distant nodes, limiting their suitability for long-range tasks. To address this, we introduce DropSens, a sensitivity-aware variant of DropEdge that explicitly controls the proportion of information lost due to edge-dropping, thereby increasing sensitivity to distant nodes despite dropping the same number of edges. Our experiments on long-range synthetic and real-world datasets confirm the predicted limitations of existing edge-dropping and feature-dropping methods. Moreover, DropSens consistently outperforms graph rewiring techniques designed to mitigate over-squashing, suggesting that simple, targeted modifications can substantially improve a model's ability to capture long-range interactions. Our conclusions highlight the need to re-evaluate and re-design existing methods for training deep GNNs, with a renewed focus on modelling long-range interactions.