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Learning to rank influential nodes in complex networks via convolutional neural networks

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Abstract

Identifying influential nodes is crucial for enhancing information diffusion in complex networks. Several approaches have been proposed to find these influential nodes based on the network structure that significantly impacts the node influence. Recently, several deep learning algorithms have also been introduced to identify influential nodes based on network exploration and node feature selection. However, this has led to challenges in enhancing efficiency and minimizing computation time. To address these challenges, we propose a novel framework called LCNN that uses convolutional neural networks and node-local representations to identify influential nodes in complex networks. We argue that we can measure node influence capacity using multi-scale metrics and a node’s adjacent matrix of one-hop neighbors to improve extracted information while reducing running time. According to the susceptible-infectious-recovered (SIR) model, the experiment results demonstrate that our proposed LCNN outperforms the state-of-the-art methods on both real-world and synthetic networks. Additionally, it exhibits a moderate time consumption, which makes it suitable for large-scale networks.

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Data Availability

The datasets used and the code generated during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://github.com/OuYangg/Multi-channel-RCNN

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their valuable comments that have helped to improve the paper.

Funding

This work is supported in part by National Nature Science Foundation of China (Grant No: 62172167).

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Authors and Affiliations

  1. Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China

    Waseem Ahmad, Bang Wang & Si Chen

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  1. Waseem Ahmad
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  2. Bang Wang
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  3. Si Chen
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Correspondence to Bang Wang.

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Ahmad, W., Wang, B. & Chen, S. Learning to rank influential nodes in complex networks via convolutional neural networks. Appl Intell 54, 3260–3278 (2024). https://doi.org/10.1007/s10489-024-05336-x

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