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A novel complex network prediction method based on multi-granularity contrastive learning

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Abstract

The rapid development of IoT, cloud computing, and big data has led to an exponential increase in data complexity, driving the widespread application of complex networks. In transportation networks, for example, accurately predicting vehicle behaviors and traffic flow is critical for optimizing intelligent transportation systems. However, traditional deep learning models often focus on a single spatial granularity, limiting their ability to fully capture the multi-granularity interactions within these networks, reducing prediction accuracy. Key challenges include managing the intricate spatiotemporal dependencies inherent in complex network predictions and effectively integrating multi-granularity information. To address these challenges, we propose a novel complex network prediction method based on spatial multi-granularity adaptive fusion and contrastive learning. Our approach captures spatial representations at three levels: micro (node-wise graph), meso (regional graph), and macro (global graph). These representations are dynamically fused through an adaptive strategy to enhance spatiotemporal modeling. Furthermore, we introduce a multi-granularity contrastive learning mechanism to explore both commonalities and distinctions across three levels, boosting the model’s robustness and generalization. By aligning and contrasting features at various granularities, our method captures both local and global dynamics effectively. Extensive experiments on three real-world traffic datasets demonstrate that our method consistently outperforms state-of-the-art models in prediction accuracy.

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Acknowledgements

This work was supported by the Heilongjiang Key R &D Program of China under Grant No. GA23A915 and the National Natural Science Foundation of China under Grant No. 62302120.

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, No. 145 Nantong Street, Harbin, 150001, Heilongjiang, China

    Shanshan Sui, Qilong Han & Dan Lu

  2. School of Computer Science, University of Technology Sydney, 15 Broadway, Sydney, NSW, 2007, Australia

    Shiqing Wu & Guandong Xu

  3. The Education University of Hong Kong, 10 Lo Ping Road, Tai Po, Hong Kong, Hong Kong SAR, China

    Guandong Xu

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  1. Shanshan Sui
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  2. Qilong Han
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  3. Dan Lu
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Correspondence to Qilong Han or Dan Lu.

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Sui, S., Han, Q., Lu, D. et al. A novel complex network prediction method based on multi-granularity contrastive learning. CCF Trans. Pervasive Comp. Interact. 6, 394–405 (2024). https://doi.org/10.1007/s42486-024-00174-9

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  • DOI: https://doi.org/10.1007/s42486-024-00174-9

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