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arxiv: 2411.17429 · v2 · submitted 2024-11-26 · 💻 cs.LG · cs.AI

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey

Hugo Attali , Davide Buscaldi , Nathalie Pernelle , Fragkiskos D. Malliaros This is my paper

Pith reviewed 2026-05-23 16:36 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords graph neural networksgraph rewiringover-squashingover-smoothingmessage passingtopology modification
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The pith

Graph rewiring changes input topology to let GNNs propagate information farther without excessive compression or node indistinguishability.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The survey reviews techniques that alter the structure of graphs before or during GNN training. These alterations target two linked problems: distant signals get squeezed too much during message passing, and repeated layers make all node features converge to the same values. Both issues trace back to how the fixed input graph interacts with the propagation rule. The authors organize existing rewiring methods by their theoretical basis, implementation details, and observed performance costs. Readers care because these fixes aim to expand the range of graphs on which standard GNNs remain effective.

Core claim

Over-squashing and over-smoothing both originate in the interaction between message passing and the given graph topology; rewiring methods modify that topology to improve long-range information flow while preserving task-relevant structure.

What carries the argument

Graph rewiring techniques: a family of methods that add, remove, or reweight edges to change the input graph's connectivity before or during GNN message passing.

If this is right

  • Rewiring can reduce the exponential compression of signals from distant nodes.
  • It can slow the convergence of node representations across layers.
  • Different rewiring strategies carry distinct theoretical guarantees and computational overheads.
  • The choice of rewiring must balance added connectivity against preservation of original task signals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Rewiring might generalize as a preprocessing step for any message-passing architecture on graphs.
  • Combining rewiring with learned edge predictors could reduce the need for manual hyper-parameter search.
  • Domain-specific graphs such as molecular or citation networks may require tailored rewiring rules that respect known constraints.

Load-bearing premise

The two performance bottlenecks are caused mainly by the fixed input topology rather than by other aspects of the model or training procedure.

What would settle it

A controlled experiment in which a rewiring method is applied yet measured long-range signal strength and node distinguishability remain unchanged or worsen.

Figures

Figures reproduced from arXiv: 2411.17429 by Davide Buscaldi, Fragkiskos D. Malliaros, Hugo Attali, Nathalie Pernelle.

Figure 1
Figure 1. Figure 1: The red edges indicate an "overload" of information within each clique, en￾hancing feature homogeneity and promoting oversmoothing. In contrast, the blue edges represent the compression of information between the two dense subgraphs [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: By reducing the density of cliques (red edges) and adding green edges around the bottleneck structure, information can flow more effectively between the two subgraphs. This setup mitigates oversquashing and oversmoothing by promoting a more balanced information flow throughout the graph Building G+ effectively necessitates a thorough understanding of how the graph structure impacts message-passing dynamics… view at source ↗
read the original abstract

Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challenges: over-squashing, where information from distant nodes is excessively compressed, and over-smoothing, where repeated propagation makes node representations indistinguishable. Both phenomena stem from the interaction between message passing and the input topology, ultimately degrading information flow and limiting the performance of GNNs. In this survey, we examine graph rewiring techniques, a class of methods designed to modify the graph topology to enhance information propagation in GNNs. We provide a comprehensive review of state-of-the-art rewiring approaches, delving into their theoretical underpinnings, practical implementations, and performance trade-offs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper is a survey reviewing graph rewiring techniques in GNNs designed to mitigate over-squashing (excessive compression of distant node information) and over-smoothing (indistinguishable node representations after repeated propagation). It covers the theoretical underpinnings of these phenomena as arising from message-passing interactions with input topology, then examines state-of-the-art rewiring methods, their implementations, and performance trade-offs.

Significance. A well-structured survey in this area would organize the rapidly growing literature on topology modification for better information flow, potentially aiding researchers in selecting methods and identifying gaps. The central premise aligns with established GNN literature on topology-message passing interactions; no novel derivations or empirical claims are made by the survey itself.

minor comments (2)
  1. [Abstract] Abstract: The scope is stated clearly, but the manuscript should explicitly state the search methodology, inclusion criteria, number of papers reviewed, and time frame covered to allow assessment of completeness and potential selection bias.
  2. The survey would benefit from a consistent taxonomy or comparison table (e.g., by rewiring type, computational cost, or empirical metrics used across cited works) to make trade-offs more transparent.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of our survey and the recommendation of minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No circularity: survey reports external literature without internal derivations

full rationale

This is a survey paper whose purpose is to review and organize existing published work on graph rewiring for GNNs. It contains no original equations, fitted parameters, predictions, or derivation chain that could reduce to its own inputs. The central premise (interaction of message passing with topology causing over-squashing and over-smoothing) is presented as established background from the broader GNN literature, not as a novel result derived or justified within the paper itself. No self-citation load-bearing steps, ansatzes, or renamings of results occur because the paper does not advance new technical claims that require such justification.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a survey paper reviewing published rewiring methods; no new free parameters, axioms, or invented entities are introduced by the authors.

pith-pipeline@v0.9.0 · 5658 in / 965 out tokens · 28849 ms · 2026-05-23T16:36:11.269664+00:00 · methodology

discussion (0)

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Forward citations

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