ARXIV:2603.11944 · GRAPH NEURAL NETWORKS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

arXiv

Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks. While recent rewiring methods attempt to alleviate this limitation, many rely on local criteria such as curvature, which can overlook global connectivity…

METHOD

Full abstract

Graph Neural Networks struggle to capture long-range dependencies due to over-squashing, where information from exponentially growing neighborhoods must pass through a small number of structural bottlenecks. While recent rewiring methods attempt to alleviate this limitation, many rely on local criteria such as curvature, which can overlook global connectivity constraints that restrict information flow. We introduce Effective Resistance Rewiring (ERR), a simple topology correction strategy that uses effective resistance as a global signal to detect structural bottlenecks. ERR iteratively adds edges between node pairs with the largest resistance while removing edges with minimal resistance, strengthening weak communication pathways while controlling graph densification under a fixed edge budget. The procedure is parameter-free beyond the rewiring budget and relies on a single global measure aggregating all paths between node pairs. Beyond predictive performance with GCN models, we analyze how rewiring affects message propagation. By tracking cosine similarity between node embeddings across layers, we examine how the relationship between initial node features and learned representations evolves during message passing, comparing graphs with and without rewiring. This analysis helps determine whether improvements arise from better long-range communication rather than changes in embedding geometry. Experiments on homophilic and heterophilic graphs, including directed settings with DirGCN, reveal a trade-off between over-squashing and oversmoothing, where oversmoothing corresponds to the loss of representation diversity across layers. Resistance-guided rewiring improves connectivity and signal propagation but can accelerate representation mixing in deep models. Combining ERR with normalization techniques such as PairNorm stabilizes this trade-off and improves performance.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. Resistance-guided rewiring improves connectivity and signal propagation but can accelerate representation mixing in deep models.

WHY NOW

Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainIntroducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.11944 · GRAPH NEURAL NETWORKS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

arXiv

Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainIntroducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.11944

Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback27 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

3.0

Time to MVP

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Commercial

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Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 3.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks. While recent rewiring methods attempt to alleviate this limitation, many rely on local criteria such as curvature, which can overlook global connectivity constraints that restrict information flow.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Graph Neural Networks struggle to capture long-range dependencies due to over-squashing, where information from exponentially growing neighborhoods must pass through a small number of structural bottlenecks. While recent rewiring methods attempt to alleviate this limitation, many rely on local criteria such as curvature, which can overlook global connectivity constraints that restrict information flow.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Resistance-guided rewiring improves connectivity and signal propagation but can accelerate representation mixing in deep models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Graph Neural Networks moved forward this cycle; last verified April 2026. Public score 3.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

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Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

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Methods

Materials

PDF linked

Markets

Graph Neural Networks

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Competitive landscape

Introducing Effective Resistance Rewiring to enhance long-range dependencies in Graph Neural Networks.

Segment

Graph Neural Networks

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

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Substitute

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Unknown

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Buzz

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References(27)

On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning

2025Alvaro Arroyo, Alessio Gravina et al.

Spectral Graph Pruning Against Over-Squashing and Over-Smoothing

2024Adarsh Jamadandi, Celia Rubio-Madrigal et al.

Limits of Depth: Over-Smoothing and Over-Squashing in GNNs

2024Aafaq Mohi ud din, Shaima Qureshi

How does over-squashing affect the power of GNNs?

2023Francesco Di Giovanni, T. Konstantin Rusch et al.

A Survey on Oversmoothing in Graph Neural Networks

2023T. Konstantin Rusch, Michael M. Bronstein et al.

Understanding Oversquashing in GNNs through the Lens of Effective Resistance

2023Mitchell Black, A. Nayyeri et al.

On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology

2023Francesco Di Giovanni, Lorenzo Giusti et al.

On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks

2022Jhony H. Giraldo, Konstantinos Skianis et al.

DiffWire: Inductive Graph Rewiring via the Lovász Bound

2022Adrián Arnaiz-Rodríguez, Ahmed Begga et al.

Not too little, not too much: a theoretical analysis of graph (over)smoothing

2022Nicolas Keriven

Discrete curvature on graphs from the effective resistance

2022K. Devriendt, R. Lambiotte

Understanding over-squashing and bottlenecks on graphs via curvature

2021Jake Topping, Francesco Di Giovanni et al.

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

2021Kaixiong Zhou, Xiao Huang et al.

On the Bottleneck of Graph Neural Networks and its Practical Implications

2020Uri Alon, Eran Yahav

Directed Graph Convolutional Network

2020Zekun Tong, Yuxuan Liang et al.

Geom-GCN: Geometric Graph Convolutional Networks

2020Hongbin Pei, Bingzhen Wei et al.

PairNorm: Tackling Oversmoothing in GNNs

2019Lingxiao Zhao, L. Akoglu

Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View

2019Deli Chen, Yankai Lin et al.

BERT Rediscovers the Classical NLP Pipeline

2019Ian Tenney, Dipanjan Das et al.

Similarity of Neural Network Representations Revisited

2019Simon Kornblith, Mohammad Norouzi et al.

Showing 20 of 27 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkUnifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks

3.0

Extension

none indexed

Commercially relevant

Higher ViabilityAdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation

5.0

Higher ViabilityReservoir-Based Graph Convolutional Networks

7.0

Higher ViabilityProbing Graph Neural Network Activation Patterns Through Graph Topology

4.0

Higher ViabilityANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling

7.0

Higher ViabilityTopology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks

7.0

Higher ViabilityCGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization

4.0

Higher ViabilityCost-Sensitive Neighborhood Aggregation for Heterophilous Graphs: When Does Per-Edge Routing Help?

4.0

Conflicting

Competing ApproachOn the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks

2.0

Competing ApproachGraph Negative Feedback Bias Correction Framework for Adaptive Heterophily Modeling

3.0

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Agent drawer

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Run context

Paper: 2603.11944
Route: /paper/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing
Active tab: read
Artifact: effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing

Available agents

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API/MCP endpoints

REST paper pack API/api/v1/paper/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing/paper-pack
REST build passport API/api/v1/paper/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

Canonical ID effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing | Route /paper/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing

MCP example

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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

/buildability/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing

Ignoreblocked

Subject: Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing

Paper ref

effective-resistance-rewiring-a-simple-topological-correction-for-over-squashing

arXiv id

2603.11944

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

4a33b712b072b04d88ebe8fd2e4e755b11a368b8c303eed21e519e1348320af0

Canonical opportunity-kernel lineage hash.

Signature state

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unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

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Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

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Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing

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