基于K-medoids和高斯核图注意力网络的社交用户地理位置.
Aobo Jiao1, Yaqiong Qiao2, Pengcheng Li3
1North China University of Water Resources and Electric Power, Zhengzhou, China.
Scientific reports
|January 13, 2026
概括
这项研究引入了社会用户地理定位的新方法,提高了农村地区的准确性. 该KMKGAT模型增强了位置特征的学习,优于现有的技术.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络工程 网络工程
背景情况:
- 准确的用户位置对于基于位置的服务至关重要.
- 目前的方法与农村地区作斗争,忽视特征距离,降低地理位置的准确性.
- 社交网络数据提供了丰富而复杂的位置相关信息.
研究的目的:
- 开发一种新的社交用户地理定位方法 (KMKGAT),克服现有方法的局限性.
- 提高用户定位的准确性,特别是在像农村地区这样具有挑战性的环境中.
- 有效地整合空间近距离和文本特征,以改善地理位置.
主要方法:
- 拟议的KMKGAT方法结合了k-medoids集群和高斯核图注意力网络.
- 利用一种抗噪声k-medoids算法来准确地对用户进行地理集群.
- 嵌入参数化的高斯内核功能在图表注意网络中学习位置增强功能.
主要成果:
- 与最先进的基线方法相比,KMKGAT表现优越.
- 该方法有效地解决了社交网络中位置特征偏差的问题.
- 在三个公共推特数据集上的实验验验证了拟议方法的有效性.
结论:
- KMKGAT方法在社交用户地理位置方面取得了重大进展.
- 精确的聚类和位置增强特征学习是提高地理位置精度的关键.
- 这种方法对更可靠的基于位置的网络服务具有前景.
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