Large-Scale Network Router-Level Topology Construction Based on Knowledge Inference

This letter presents a knowledge inference-based (KI) method for topology construction. KI builds a multi-source heterogeneous network knowledge graph, integrating multi-modal data, including IP interface-level topology, geospatial information, and autonomous system affiliations, to achieve a semantic representation of network topology. A hierarchical-aware embedding model (HAKE) is utilized to extract semantic features of topology entities and encode them as polar coordinate vectors. By incorporating routing protocol constraints, the tasks of IP alias resolution and anonymous router identification are reformulated as a constrained maximum likelihood estimation problem, enabling joint inference and completion of topology relationships. Experimental results demonstrate that the proposed method significantly enhances topology reconstruction accuracy across various scenarios with different anonymous interface ratios and probing intensities while maintaining robust performance even in high anonymous interface ratio conditions.