Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

Identifying a maximum independent set is a fundamental NP-hard problem. Therefore, we use a population-based genetic algorithm to evolve the model’s parameters instead.

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• 1 This problem has several real-world applications and requires finding the largest possible set of vertices not adjacent to each other in an undirected graph.
• 2 Over the past few years, branch-and-bound and branch-and-reduce algorithms have emerged as some of the most effective methods for solving the problem exactly.
• 3 Specifically, the branch-and-reduce approach, which combines branch-and-bound principles with reduction rules, has proven particularly successful in tackling previously unmanageable real-world instances.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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