用地理加权图神经网络提升网络犯罪预测准确率
Cybercrime Prediction via Geographically Weighted Learning
- 引入地理坐标作为权重，构建考虑空间连续性的图神经网络
- 在海湾地区数据上实现4分类准确率超越传统模型
- 理论证明该方法在空间数据分类中具有更高潜在精度
受地理加权回归成功经验启发，我们提出GeogGNN——一种考虑经纬度坐标的图神经网络模型。基于合成生成的数据集，针对以海湾合作委员会地区为中心、带有真实感地理坐标的网络安全4类分类问题进行实验。结果表明，该模型在分类准确率上优于将坐标作为普通特征处理的标准神经网络和卷积神经网络。鉴于模型性能的显著提升，我们进一步给出一个通用数学结论：利用空间连续性与局部平均特性，几何加权神经网络在原则上始终能提升空间相关数据的分类准确率。
原文摘要 · Abstract (English)
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.
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