ICML 2025

Improving the Effective Receptive Field of Message-Passing Neural Networks
Shahaf E. Finder 1 2 Ron Shapira Weber 1 2 Moshe Eliasof 3 Oren Freifeld 1 2 4 Eran Treister 1 2
Abstract
Message-Passing Neural Networks (MPNNs)
have become a cornerstone for processing and an-
alyzing graph-structured data. However, their ef-
fectiveness is often hindered by phenomena such
as over-squashing, where long-range dependen-
cies or interactions are inadequately captured and
expressed in the MPNN output. This limitation
mirrors the challenges of the Effective Receptive
Field (ERF) in Convolutional Neural Networks
(CNNs), where the theoretical receptive field is
underutilized in practice. In this work, we show
and theoretically explain the limited ERF prob-
lem in MPNNs.
Furthermore, inspired by re-
cent advances in ERF augmentation for CNNs,
we propose an Interleaved Multiscale Message-
Passing Neural Networks (IM-MPNN) architec-
ture to address these problems in MPNNs. Our
method incorporates a hierarchical coarsening
of the graph, enabling message-passing across
multiscale representations and facilitating long-
range interactions without excessive depth or pa-
rameterization. Through extensive evaluations
on benchmarks such as the Long-Range Graph
Benchmark (LRGB), we demonstrate substantial
improvements over baseline MPNNs in captur-
ing long-range dependencies while maintaining
computational efficiency.
1. Introduction
Graph Neural Networks (GNNs) have emerged as power-
ful tools for solving a wide range of problems modeled
as graphs. Applications span diverse domains, including
combinatorial optimization (Cappart et al., 2023), particle
1Department of Computer Science, Ben-Gurion University, Is-
rael 2Data Science Research Center, Ben-Gurion University, Israel
3Department of Applied Mathematics and Theoretical Physics,
University of Cambridge, United Kingdom 4School of Brain Sci-
ences and Cognition, Ben-Gurion University, Israel. Correspon-
dence to: Shahaf E. Finder <finder...