Inspired by the success of Geographically Weighted Regression and its accounting for spatial variations, we propose GeogGNN -- A graph neural network model that accounts for geographical latitude and longitudinal points. Using a synthetically generated dataset, we apply the algorithm for a 4-class classification problem in cybersecurity with seemingly realistic geographic coordinates centered in the Gulf Cooperation Council region. We demonstrate that it has higher accuracy than standard neural networks and convolutional neural networks that treat the coordinates as features. Encouraged by the speed-up in model accuracy by the GeogGNN model, we provide a general mathematical result that demonstrates that a geometrically weighted neural network will, in principle, always display higher accuracy in the classification of spatially dependent data by making use of spatial continuity and local averaging features.

本研究解决了传统神经网络在处理空间依赖数据时准确性不足的问题，通过提出GeogGNN模型，考虑地理坐标的影响，进行网络安全的四类分类。实验表明，相比于标准神经网络和卷积神经网络，GeogGNN模型的准确性更高，显示出在空间依赖数据分类中的优势潜力。

基于地理加权学习的网络犯罪预测