Zhiyuan Wang
Fan Zhou
ABSTRACT
Pinpointing the geographic location of an IP address is important for a range of location-aware applications spanning from targeted advertising to fraud prevention. The majority of traditional measurement-based and recent learning-based methods either focus on the efficient employment of topology or utilize data mining to find clues of the target IP in publicly available sources. Motivated by the limitations in existing works, we propose a novel framework named GraphGeo, which provides a complete processing methodology for street-level IP geolocation with the application of graph neural networks. It incorporates IP hosts knowledge and kinds of neighborhood relationships into the graph to infer spatial topology for high-quality geolocation prediction. We explicitly consider and alleviate the negative impact of uncertainty caused by network jitter and congestion, which are pervasive in complicated network environments. Extensive evaluations across three large-scale real-world datasets demonstrate that GraphGeo significantly reduces the geolocation errors compared to the state-of-the-art methods. Moreover, the proposed framework has been deployed on the web platform as an online service for 6 months.
Supplemental Material
- Sercan Ö. Arik and Tomas Pfister. 2021. TabNet: Attentive Interpretable Tabular Learning. In AAAI. 6679--6687.Google Scholar
- Uri M Ascher, Steven J Ruuth, and Raymond J Spiteri. 1997. Implicit-explicit Runge-Kutta methods for time-dependent partial differential equations. Appl. Numer. Math. (1997), 151--167.Google Scholar
- Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural ordinary differential equations. In NeurIPS. 6572--6583.Google Scholar
- Fan RK Chung and Fan Chung Graham. 1997. Spectral graph theory. American Mathematical Soc.Google Scholar
- Ovidiu Dan, Vaibhav Parikh, and Brian D Davison. 2021. IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation. In WWW. 332--338.Google Scholar
- Brian Eriksson, Paul Barford, Joel Sommers, and Robert Nowak. 2010. A learning-based approach for IP geolocation. In PAM. 171--180.Google Scholar
- Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW. 417--426.Google Scholar
- Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi. 2021. SLAPS: Self- Supervision Improves Structure Learning for Graph Neural Networks. In NeurIPS.Google Scholar
- Chen Gao, Yu Zheng, Nian Li, Yinfeng Li, Yingrong Qin, Jinghua Piao, Yuhan Quan, Jianxin Chang, Depeng Jin, Xiangnan He, and Yong Li. 2021. Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions. arXiv:2109.12843Google Scholar
- Bamba Gueye, Artur Ziviani, Mark Crovella, and Serge Fdida. 2006. IEEE/ACM Trans. Netw. Transactions On Networking (2006), 1219--1232.Google Scholar
- William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024--1034.Google Scholar
- Peter Hillmann, Lars Stiemert, Gabi Dreo, and Oliver Rose. 2020. On the Path to High Precise IP Geolocation: A Self-Optimizing Model. arXiv:2004.01531Google Scholar
- Peter Hillmann, Lars Stiemert, Gabi Dreo Rodosek, and Oliver Rose. 2015. Modelling of IP Geolocation by use of Latency Measurements. In CNSM. 173--177.Google Scholar
- Bradley Huffaker, Marina Fomenkov, and KC Claffy. 2014. DRoP: DNS-based router positioning. Comput. Commun. Rev. (2014), 5--13.Google Scholar
- Hao Jiang, Yaoqing Liu, and Jeanna N Matthews. 2016. IP geolocation estimation using neural networks with stable landmarks. In INFOCOM WKSHPS. 170--175.Google Scholar
- Weiwei Jiang and Jiayun Luo. 2021. Graph Neural Network for Traffic Forecasting: A Survey. arXiv:2101.11174Google Scholar
- Ethan Katz-Bassett, John P John, Arvind Krishnamurthy, David Wetherall, Thomas Anderson, and Yatin Chawathe. 2006. Towards IP geolocation using delay and topology measurements. In SIGCOMM. 71--84.Google Scholar
- Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In NeurIPS. 3146--3154.Google Scholar
- Diederik P Kingma and Max Welling. 2014. Auto-encoding variational bayes. In ICLR.Google Scholar
- Thomas N. Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. arXiv:1611.07308Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR.Google Scholar
- Sándor Laki, Péter Mátray, Péter Hága, Tamás Sebok, István Csabai, and Gábor Vattay. 2011. Spotter: A model based active geolocation service. In INFOCOM. 3173--3181.Google Scholar
- Qiang Li, Zhihao Wang, Dawei Tan, Jinke Song, Haining Wang, Limin Sun, and Jiqiang Liu. 2021. GeoCAM: An IP-Based Geolocation Service Through Fine- Grained and Stable Webcam Landmarks. IEEE/ACM Trans. Networking (2021), 1798--1812.Google Scholar
- Dawen Liang, Rahul G Krishnan, Matthew D Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In WWW. 689--698.Google Scholar
- Hao Liu, Yaoxue Zhang, Yuezhi Zhou, Di Zhang, Xiaoming Fu, and KK Ramakrishnan. 2014. Mining checkins from location-sharing services for client-independent ip geolocation. In INFOCOM. 619--627.Google Scholar
- David Moore, Ram Periakaruppan, Jim Donohoe, and Kimberly Claffy. 2000. Where in the world is netgeo.caida.org?. In INET.Google Scholar
- Venkata N. Padmanabhan and Lakshminarayanan Subramanian. 2001. An investigation of geographic mapping techniques for internet hosts. In SIGCOMM. 173--185.Google Scholar
- Afshin Rahimi, Trevor Cohn, and Timothy Baldwin. 2018. Semi-supervised User Geolocation via Graph Convolutional Networks. In ACL. 2009--2019.Google Scholar
- Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar. 2020. Composition-based Multi-Relational Graph Convolutional Networks. In ICLR.Google Scholar
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS. 5998--6008.Google Scholar
- Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.Google Scholar
- Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.Google Scholar
- Yong Wang, Daniel Burgener, Marcel Flores, Aleksandar Kuzmanovic, and Cheng Huang. 2011. Towards Street-Level Client-Independent IP Geolocation.. In NSDI. USENIX.Google Scholar
- Yucheng Wang, Hongsong Zhu, Jinfa Wang, Jie Liu, Yong Wang, and Limin Sun. 2020. XLBoost-Geo: An IP Geolocation System Based on Extreme Landmark Boosting. arXiv:2010.13396Google Scholar
- Zhihao Wang, Qiang Li, Jinke Song, Haining Wang, and Limin Sun. 2020. Towards IP-based geolocation via fine-grained and stable webcam landmarks. In WWW. 1422--1432.Google Scholar
- Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do Transformers Really Perform Badly for Graph Representation?. In NeurIPS.Google Scholar
- Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In SIGKDD. 793--803.Google Scholar
- Qian Zhao, Fei Wang, Can Huang, and Chuan Yu. 2020. Improving IP geolocation databases based on multi-method classification. In ASID. 44--48.Google Scholar
- Ting Zhong, Tianliang Wang, Fan Zhou, Goce Trajcevski, Kunpeng Zhang, and Yi Yang. 2020. Interpreting Twitter user geolocation. In ACL. 853--859.Google Scholar
- Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In SIGKDD. 1399--1407.Google Scholar
- Artur Ziviani, Serge Fdida, José F De Rezende, and Otto Carlos MB Duarte. 2005. Improving the accuracy of measurement-based geographic location of Internet hosts. Comput. Networks (2005), 503--523.Google Scholar
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