Equivariant Neural Primal-Dual Assignment for Maximum Common Edge Subgraphs

arXiv:2609.32661 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/jiaqingxie/ENPDA-MCES

Terminology

• Core — 49× in this paper · explained in 2 episodes
• Accuracy — 41× in this paper · explained in 1 episode
• Equivariant Neural Primal-Dual Assignment — 37× in this paper
• Benchmark — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• GNNs — 2× in this paper · explained in 2 episodes
• Graph Matching Networks — 2× in this paper
• Neural Graph Matching Network — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Amortization — 1× in this paper · explained in 1 episode
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• New Auction Algorithms for the Assignment Problem and Extensions
• TUDataset: A collection of benchmark datasets for learning with graphs
• Neural Subgraph Matching

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