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.
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 7270-7274 |
| Number of pages | 5 |
| ISBN (Electronic) | 9798350344851 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |
| Event | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of Duration: 14 Apr 2024 → 19 Apr 2024 |
Publication series
| Name | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
|---|---|
| ISSN (Print) | 1520-6149 |
Conference
| Conference | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | Seoul |
| Period | 14/04/24 → 19/04/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Street-level IP geolocation
- explainability
- graph neural network
- information bottleneck theory