Identify Critical Nodes in Complex Network with Large Language Models
Jinzhu Mao, Dongyun Zou, Li Sheng, Siyi Liu, Chen Gao, Yue Wang, Yong, Li
TL;DR
This paper introduces a novel method that combines Large Language Models with Evolutionary Algorithms to identify critical nodes in complex networks, demonstrating superior performance and generalization over existing algorithms.
Contribution
It presents a new approach integrating LLMs with EAs to generate effective node scoring functions for network analysis, which is a significant advancement over prior methods.
Findings
Outperforms state-of-the-art algorithms in node identification tasks.
Demonstrates strong generalization and diversity in generated scoring functions.
Provides publicly available code and models for reproducibility.
Abstract
Identifying critical nodes in networks is a classical decision-making 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. It evolves from initial populations with a set of designed node scoring functions created manually. LLMs leverage their strong contextual understanding and rich programming skills to perform crossover and mutation operations on the individuals, generating excellent new functions. These functions are then categorized, ranked, and eliminated to ensure the stable development of the…
Peer Reviews
Decision·Submitted to ICLR 2025
1. The paper presents a novel approach to identifying critical nodes in a network using LLMs for code generation. 2. The authors cast the critical node generation problem as a code generation problem where LLMs can generate node scoring functions that can potentially adapt to different network structures. 3. The authors introduce a population management approach that uses semantic analysis and metrics on function embeddings to maintain diversity in the function population. 4. The proposed app
1. The authors may consider testing LLM’s robustness for varying network structures and properties and share the insights and the limitations. 2. The authors may consider implementing an interpretability mechanism, such as feature importance analysis, to understand which generated functions are more effective on which network structures. 3. The authors are encouraged to provide a more comprehensive discussion of the limitations of this approach.
1. Introduces a new perspective by integrating LLMs with EAs to transform the critical node detection problem into a code-generation task. 2. Proposes a general framework that combines LLMs and evolutionary learning, along with a generation-update mechanism for LLMs and crossover-mutation methods for evolutionary learning.
1. The experimental results show that the author's method significantly outperforms other baselines, while the performance of all other baselines is similar. This raises concerns about the experimental results, as the author does not provide a detailed analysis or explanation of the source of such a significant improvement (i.e., which computational processes contribute to it). 2. Ablation studies indicate that manual initialization has a substantial impact on the method's effectiveness. This
- The paper is relatively easy to follow. - Comparisons are made to a large number of baselines, proving experimental rigor. - The results are consistent, and prove the key conclusions of the paper well. - I appreciate the function analysis and case study, showing that techniques based on code generation can improve on the interpretability of our machine learning methods.
- While the use-case is novel to my knowledge, the use of an LLM as the mutation operator for an evolutionary algorithm framework is not novel, and has not been adequately framed in relation to prior work (ELM, etc). [1] - This work seems like a very narrow use case. I would have liked to see whether this method can adapt to other network based domains. - Although there is an ablation study, I don’t find it convincing as evidence for the use of such as sophisticated population management frame
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Taxonomy
TopicsOpinion Dynamics and Social Influence
MethodsSparse Evolutionary Training