G-NAC: Graph Neural Automata Clustering via Emergent Domain Formation

arXiv:2609.24823 · cs.LG, eess.SP · Submitted 2026-09-21 · Read on arXiv

cs.LG, eess.SP

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: 39 pages, 2 figures

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

Terminology

• Clustering — 131× in this paper · explained in 2 episodes
• Benchmark — 47× in this paper · explained in 2 episodes
• Spectral clustering — 25× in this paper · explained in 2 episodes
• Regularization — 12× in this paper · explained in 3 episodes
• Graph Construction — 10× in this paper · explained in 3 episodes
• Graph Neural Cellular Automata — 8× in this paper
• Graph Neural Automata Clustering — 7× in this paper
• Robustness — 6× in this paper · explained in 5 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Deep Embedded Clustering — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Adjusted Rand Index — 2× in this paper
• Aggregation — 2× in this paper · explained in 1 episode
• Improved Deep Embedded Clustering — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Activation Checkpointing — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adjusted Mutual Information — 1× in this paper
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Dynamical regimes — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Fundamental Clustering and Projection Suite — 1× in this paper
• Modularity — 1× in this paper · explained in 9 episodes
• Normalized Clustering Accuracy — 1× in this paper
• Objective Function — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Self-Organizing Map — 1× in this paper
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Symmetry Breaking — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 2 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks