Identify Critical Nodes in Complex Network with Large Language Models
Keywords: Complex Networks, Large Language Models
TL;DR: We propose a novel large language model-based approach for the problem of critical-node selection on complex networks.
Abstract: Identifying critical nodes in networks is a classical combinatorial optimization task, and many methods struggle to strike a balance between adaptability and utility. Therefore, we propose an approach that empowers Evolutionary Algorithm (EA) with Large Language Models (LLMs), to generate a function called "score_nodes" which can further be used to identify crucial nodes based on their assigned scores. Our model consists of three main components: Manual Initialization, Population Management, and LLMs-based Evolution, and it evolves from initial populations with a set of designed node scoring functions created manually. LLMs leverage their strong contextual understanding and rich programming techniques to perform crossover and mutation operations on the individuals, generating new functions. These functions are then categorized, ranked, and eliminated to ensure the stable development of the populations while preserving diversity. Extensive experiments demonstrate the excellent performance of our method compared to other state-of-the-art algorithms. It can generate diverse and efficient node scoring functions to identify critical nodes in the network.
Supplementary Material: zip
Primary Area: optimization
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Submission Number: 11156
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