Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture
📰 ArXiv cs.AI
Learn to select the best Maximum Clique algorithm using a dual-channel hybrid neural architecture for improved performance on diverse graph instances
Action Steps
- Build a dataset of graph instances with corresponding Maximum Clique solutions
- Train a traditional machine learning model to predict algorithm performance
- Implement a dual-channel hybrid neural architecture to learn instance-aware algorithm selection
- Compare the performance of different algorithms on various graph instances using the learned model
- Apply the selected algorithm to solve the Maximum Clique Problem on new, unseen graph instances
Who Needs to Know This
Researchers and developers working on graph-based problems, such as bioinformatics and network science, can benefit from this approach to improve algorithm selection and overall performance
Key Insight
💡 A dual-channel hybrid neural architecture can effectively learn to select the best Maximum Clique algorithm for a given graph instance, outperforming traditional machine learning approaches
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🤖 Learn to select the best Maximum Clique algorithm using a dual-channel hybrid neural architecture! 📈 Improve performance on diverse graph instances #MachineLearning #GraphTheory
Key Takeaways
Learn to select the best Maximum Clique algorithm using a dual-channel hybrid neural architecture for improved performance on diverse graph instances
Full Article
Title: Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture
Abstract:
arXiv:2508.08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances. This underscores the critical need for instance-aware algorithm selection, a domain that remains largely unexplored for the MCP. To address this gap, we propose a novel learning-based framewo
Abstract:
arXiv:2508.08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances. This underscores the critical need for instance-aware algorithm selection, a domain that remains largely unexplored for the MCP. To address this gap, we propose a novel learning-based framewo
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