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Enhanced edge convolution-based spatial-temporal network for network traffic prediction

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Abstract

Accurately predicting network traffic is helpful for improving a variety of spatial-temporal data mining applications, such as intelligent traffic control, network planning and anomaly detection. The mainstream graph-based methods are limited by the node-level message passing mechanism and require transforming dimensions to generate edge representations. Accordingly, some researchers propose using edge convolution to directly learn edge representations for network traffic prediction. However, node-level and edge-level information aggregation are two different perspectives, and their combination of them can achieve better performance. This paper proposes a novel model for network traffic prediction named the Enhanced Edge Convolution-based Spatial-Temporal Network (EESTN). Armed with a Graph Neural Network and Hypergraph Neural Network, EESTN employs the edge convolutions defined on the graph and hypergraph to effectively extract spatial features. EESTN further combines node convolution to capture the complex correlations among nodes and utilizes an attention mechanism to generate the edge convolution kernel for the decoder. Moreover, a 3D convolution-based multihead self-attention mechanism and a hierarchical loss function are proposed to capture the long-term temporal dependence and make full use of the model’s represent ability. Finally, we conduct extensive experiments to validate the effectiveness of EESTN, and the related results demonstrate that EESTN outperforms the state-of-the-art methods.

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Data Availability

All datasets that support the findings of this study are available on the websites “http://sndlib.zib.de/” and “https://github.com/deepkashiwa20/ODCRN/”.

Notes

  1. https://github.com/deepkashiwa20/ODCRN/tree/main/model.

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Funding

No funding was received to assist with the preparation of this manuscript. The authors have no relevant financial or nonfinancial interests to disclose.

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Authors and Affiliations

  1. Research Institute of China Telecom Corporation Ltd., Guangzhou, China

    Zehua Hu, Ke Ruan & Weihao Yu

  2. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China

    Siyuan Chen

Authors
  1. Zehua Hu
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  2. Ke Ruan
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  3. Weihao Yu
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  4. Siyuan Chen
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Corresponding author

Correspondence to Ke Ruan.

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This research has no potential conflicts of interest and does not involve human participants or animals. Moreover, all the datasets used in our experiment are public.

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Hu, Z., Ruan, K., Yu, W. et al. Enhanced edge convolution-based spatial-temporal network for network traffic prediction. Appl Intell 53, 22031–22043 (2023). https://doi.org/10.1007/s10489-023-04626-0

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