Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture
arXiv:2508.08005v4 Announce Type: replace 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 framework that integrates both traditional machine learning and graph neural networks. We first construct a bench
The increasing complexity and scale of real-world graphs demand more sophisticated and adaptive algorithms for NP-hard problems, driving innovation in AI-based algorithm selection.
This development could significantly improve the efficiency and applicability of solving complex graph problems in diverse fields, accelerating scientific discovery and technological optimization.
The approach to intractable problems like the Maximum Clique Problem is evolving from fixed algorithms to learning-based, instance-aware selection, potentially yielding more robust and efficient solutions.
- · Bioinformatics researchers
- · Network science analysts
- · Social computing platforms
- · Machine learning researchers
- · Developers of general-purpose, non-adaptive algorithms
More efficient solutions for NP-hard problems across various scientific and industrial applications.
Acceleration of research and development in areas heavily reliant on graph analysis, such as drug discovery and social network optimization.
The development of highly specialized AI systems capable of self-optimizing problem-solving strategies, leading to new forms of autonomous agents.
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Read at arXiv cs.LG