Effects of relational graph modularity and depth on the learning performance of neural networks

arXiv:2507.10005 · cs.LG, cond-mat.stat-mech, cs.NE, physics.comp-ph · Submitted 2025-07-14 · Read on arXiv

cs.LG, cond-mat.stat-mech, cs.NE, physics.comp-ph

Submitted: 2025-07-14

Updated: 2026-09-08

Comments: 12 pages, 7 figures

Code: https://github.com/yasharyaa/Simplified_LFR_Benchmark_

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Modularity — 11× in this paper · explained in 9 episodes
• Generalization — 6× in this paper · explained in 13 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Over-smoothing — 5× in this paper · explained in 4 episodes
• Sparsity — 5× in this paper · explained in 4 episodes
• Clustering — 3× in this paper · explained in 2 episodes
• Reinforcement Learning — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• GNNs — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• A Survey on Oversmoothing in Graph Neural Networks
• Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

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