With the development of the information society, the importance of network security has become increasingly prominent, and the accurate geographical location of network entities can help better implement network management. The existing classical network entity location method based on topology heuristic clustering adopts the clustering based on network structure to cluster network entities, does not consider the specific characteristics of the network topology, and leads to final results with big error. In order to solve this problem, we propose a target area network topology partitioning method based on the motifs. According to the high clustering characteristics of local nodes in the target network topology, we innovatively introduce the concept of "motif" and analyze the motif structure in the target network topology. Learning from the idea of the initial seed expansion in local community discovery methods for the complex network, we take the motif structure as the initial seed to expand, and divide the nodes closely connected with the motif in the topology into different sets. Finally we locate the node sets according to the landmark and the public IP geo-location database, and take the location of the set as the geographic location of the nodes within it so as to achieve the bulk positioning of network entities. Experiments based on the network topologies in Hong Kong and Taiwan show that compared with the classical HCBased method and network node clustering method (NNC), the positioning accuracy of network entities of our method can be enhanced by  about 25% and 16% respectively, and there are more network entities can be located in a batch way.

 

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