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HFEPX · Eval paper review

Probing Graph Neural Network Activation Patterns Through Graph Topology

Floriano Tori, Lorenzo Bini, Marco Sorbi, Stéphane Marchand-Maillet +1 more

Published

Feb 24, 2026

Citations

0

Trust level

Moderate

Usefulness score

55/100 (Medium)

Extraction confidence

65% (Moderate)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Feb 24, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Automatic Metrics General Pairwise Preference

Should you rely on this paper?

This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.

Use this for comparison and orientation, not as your only source.

Best use

Secondary protocol comparison source

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The abstract does not clearly name benchmarks or metrics.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
55/100
Moderate-confidence candidate

Useful as a secondary reference; validate protocol details against neighboring papers.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions. Artifacts of the message passing paradigm in Graph Neural Networks, such as oversmoothing and oversquashing, have been attributed to these regions. However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs. Through Massive Activations, which correspond to extreme edge activation values in Graph Transformers, we probe this correspondence. Our findings on synthetic graphs and molecular benchmarks reveal that MAs do not preferentially concentrate on curvature extremes, despite their theoretical link to information flow. On the Long Range Graph Benchmark, we identify a systemic \textit{curvature shift}: global attention mechanisms exacerbate topological bottlenecks, drastically increasing the prevalence of negative curvature. Our work reframes curvature as a diagnostic probe for understanding when and why graph learning fails.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Pairwise Preference

Directly usable for protocol triage.

"Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Pairwise Preference
Rater population
Not reported
Expertise required
General
Evaluation details
Evaluation modes
Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
Moderate
Use this page as
Secondary protocol comparison source

Research brief

Metadata summary

Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions.
  • Artifacts of the message passing paradigm in Graph Neural Networks, such as oversmoothing and oversquashing, have been attributed to these regions.
  • However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • Curvature notions on graphs provide a theoretical description of graph topology, highlighting bottlenecks and denser connected regions.
  • Artifacts of the message passing paradigm in Graph Neural Networks, such as oversmoothing and oversquashing, have been attributed to these regions.
  • However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs.

Why it matters for eval

  • However, it remains unclear how the topology of a graph interacts with the learned preferences of GNNs.
  • Our findings on synthetic graphs and molecular benchmarks reveal that MAs do not preferentially concentrate on curvature extremes, despite their theoretical link to information flow.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Pairwise Preference

  • Evaluation mode is explicit

    Detected: Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.