GNNTAL:A Novel Model for Identifying Critical Nodes in Complex Networks

TL;DR

GNNTAL introduces an active learning model combining GraphSAGE and Transformer techniques to efficiently identify critical nodes in complex networks, reducing training costs and capturing both local and global features.

Contribution

The paper presents a novel active learning approach that pre-trains on synthetic networks and fine-tunes on real networks, improving efficiency and effectiveness in critical node detection.

Findings

GNNTAL outperforms existing methods on twelve real-world networks.

The influence maximization method based on GNNTAL achieves optimal results.

The approach reduces training costs while maintaining high accuracy.

Abstract

Identification of critical nodes is a prominent topic in the study of complex networks. Numerous methods have been proposed, yet most exhibit inherent limitations. Traditional approaches primarily analyze specific structural features of the network; however, node influence is typically the result of a combination of multiple factors. Machine learning-based methods struggle to effectively represent the complex characteristics of network structures through suitable embedding techniques and require substantial data for training, rendering them prohibitively costly for large-scale networks. To address these challenges, this paper presents an active learning model based on GraphSAGE and Transformer, named GNNTAL. This model is initially pre-trained on random or synthetic networks and subsequently fine-tuned on real-world networks by selecting a few representative nodes using K-Means clustering and uncertainty sampling. This approach offers two main advantages: (1) it significantly reduces training costs; (2) it simultaneously incorporates both local and global features. A series of comparative experiments conducted on twelve real-world networks demonstrate that GNNTAL achieves superior performance. Additionally, this paper proposes an influence maximization method based on the predictions of the GNNTAL model, which achieves optimal performance without the need for complex computations. Finally, the paper analyses certain limitations of the GNNTAL model and suggests potential solutions.