Exploring Self-Explainable Street-Level IP Geolocation with Graph Information Bottleneck

Kai Yang, Wenxin Tai, Zhenhui Li, Ting Zhong, Guangqiang Yin, Yong Wang, Fan Zhou

Abstract: Accurate IP geolocation is crucial for location-aware applications. While recent advances in router-centric IP graph methods have garnered attention, they face two persistent challenges: (1) the sparsity problem of IP graphs in rural areas and (2) the limited explainability of current IP geolocation systems. To tackle these issues, we present ExGeo, a novel and explainable graph-based approach for IP geolocation. Specifically, we introduce a target-centric IP graph, reducing sparsity and enhancing contextual information utilization. Additionally, we endow the model with explainability through a variational graph information bottleneck strategy. Experiments on three real-world datasets demonstrate significant accuracy and explainability improvements. Source code is released at https://github.com/ICDM-UESTC/ExGeo.