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Learning to Select MCP Algorithms: From Traditional ML to Dual-Channel GAT-MLP

arXiv — cs.LG•Tuesday, December 9, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • A novel learning-based framework has been proposed to address the Maximum Clique Problem (MCP), an NP-hard problem with significant applications. This framework integrates traditional machine learning techniques and graph neural networks, specifically utilizing a dual-channel model known as GAT-MLP, which combines a Graph Attention Network with a Multilayer Perceptron to enhance algorithm selection based on graph instance characteristics.
  • The development of this framework is crucial as it aims to improve algorithm performance for the MCP by leveraging instance-aware selection, which has been largely unexplored. By establishing a benchmark dataset and identifying key predictors of performance, the research highlights the importance of connectivity and topological features in determining algorithm efficacy.
  • This advancement reflects a broader trend in artificial intelligence where hybrid models that combine traditional machine learning with modern neural network architectures are gaining traction. The integration of attention mechanisms, as seen in both the GAT-MLP and other recent models, underscores a growing recognition of the need for adaptive and context-aware approaches in complex problem-solving scenarios across various domains.
— via World Pulse Now AI Editorial System
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