GSASN:一个图表自学注意力得分网络用于空间建模网络流量矩阵预测
Juan Wu1, Chensheng Tong2, Jinsong Hu2
1China Telecom Research Institute, Guangzhou, 510000, China.
概括
本研究介绍了图形自学注意力评分网络 (GSASN),以改进网络流量预测. GSASN克服了现有模型的局限性,提高了资源规划和网络性能.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 网络流量预测对于面临越来越多数据和视频需求的智能路由器至关重要.
- 目前的空间模型,如GCN和GAT,由于注意力机制的线性约束,与非线性空间关系作斗争.
- 准确的预测对于资源规划,带宽管理和延迟减少至关重要.
研究的目的:
- 开发一种新的网络流量预测模型,克服现有的空间方法的局限性.
- 引入一种自我学习的注意力机制,捕捉复杂的非线性空间依赖.
- 提出一个综合的时空模型,以提高预测的准确性和效率.
主要方法:
- 提出了图形自学注意力得分网络 (GSASN),具有可学习的参数矩阵,可以自主学习注意力得分.
- 开发了一个时空模型 (ST-GSASN),通过将GSASN与时间学习通过封闭融合进行集成.
- 在两个真实世界的网络流量数据集上验证了模型.
主要成果:
- 与图表注意网络 (GAT) 相比,GSASN显著改善,将RMSE降低了高达24.7%,MAE降低了36.5%.
- 在预测准确度方面,ST-GSASN的表现超过了基线和最先进的方法,包括基于变压器的模型.
- ST-GSASN实现了卓越的计算效率,只需要5%的可比模型的参数.
结论:
- 拟议的GSASN有效地捕捉了网络流量的复杂非线性空间关系.
- ST-GSASN为时空网络流量预测提供了一个高度准确和计算高效的解决方案.
- 新的自我学习注意力机制为未来的网络智能研究提供了有希望的方向.
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