  Rule-based fine-grained IP geolocation methods are hard to generalize in
computer networks which do not follow hypothetical rules. Recently, deep
learning methods, like multi-layer perceptron (MLP), are tried to increase
generalization capabilities. However, MLP is not so suitable for
graph-structured data like networks. MLP treats IP addresses as isolated
instances and ignores the connection information, which limits geolocation
accuracy. In this work, we research how to increase the generalization
capability with an emerging graph deep learning method -- Graph Neural Network
(GNN). First, IP geolocation is re-formulated as an attributed graph node
regression problem. Then, we propose a GNN-based IP geolocation framework named
GNN-Geo. GNN-Geo consists of a preprocessor, an encoder, messaging passing (MP)
layers and a decoder. The preprocessor and encoder transform measurement data
into the initial node embeddings. MP layers refine the initial node embeddings
by modeling the connection information. The decoder maps the refined embeddings
to nodes' locations and relieves the convergence problem by considering prior
knowledge. The experiments in 8 real-world IPv4/IPv6 networks in North America,
Europe and Asia show the proposed GNN-Geo clearly outperforms the state-of-art
rule-based and learning-based baselines. This work verifies the great potential
of GNN for fine-grained IP geolocation.
