PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (27)

[1]
Position: Don't be Afraid of Over-Smoothing And Over-Squashing
2026Niklas Kormann, Benjamin Doerr et al.
[2]
Oversmoothing, "Oversquashing", Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning
2025Adrián Arnaiz-Rodríguez, Federico Errica
[3]
Massive Activations in Graph Neural Networks: Decoding Attention for Domain-Dependent Interpretability
2024Lorenzo Bini, M. Sorbi et al.
[4]
The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited
2024Floriano Tori, Vincent Holst et al.
[5]
Massive Activations in Large Language Models
2024Mingjie Sun, Xinlei Chen et al.
[6]
Efficient Streaming Language Models with Attention Sinks
2023Guangxuan Xiao, Yuandong Tian et al.
[7]
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
2023Jan Tonshoff, Martin Ritzert et al.
[8]
How does over-squashing affect the power of GNNs?
2023Francesco Di Giovanni, T. Konstantin Rusch et al.
[9]
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
2023Mitchell Black, A. Nayyeri et al.
[10]
Leave Graphs Alone: Addressing Over-Squashing without Rewiring
2022Mateusz Malinowski, Andrea Tacchetti et al.
[11]
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks
2022Jhony H. Giraldo, Konstantinos Skianis et al.
[12]
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs
2022Kedar Karhadkar, P. Banerjee et al.
[13]
Long Range Graph Benchmark
2022Vijay Prakash Dwivedi, Ladislav Rampášek et al.
[14]
Recipe for a General, Powerful, Scalable Graph Transformer
2022Ladislav Rampášek, Mikhail Galkin et al.
[15]
Understanding over-squashing and bottlenecks on graphs via curvature
2021Jake Topping, Francesco Di Giovanni et al.
[16]
GraphiT: Encoding Graph Structure in Transformers
2021G. Mialon, Dexiong Chen et al.
[17]
Rethinking Graph Transformers with Spectral Attention
2021Devin Kreuzer, D. Beaini et al.
[18]
A Generalization of Transformer Networks to Graphs
2020Vijay Prakash Dwivedi, X. Bresson
[19]
On the Bottleneck of Graph Neural Networks and its Practical Implications
2020Uri Alon, Eran Yahav
[20]
Open Graph Benchmark: Datasets for Machine Learning on Graphs
2020Weihua Hu, Matthias Fey et al.

Showing 20 of 27 references

Founder's Pitch

"Utilize graph topology curvature as a diagnostic probe to enhance graph neural network learning insights."

Graph Neural NetworksScore: 4View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

2/4 signals

5

Series A Potential

0/4 signals

0

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 2/24/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.