Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems
Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems
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DOI：
10.1109/tnet.2018.2888600
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发表时间：
2019-02
期刊：
影响因子：
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通讯作者：
Taha Bouchoucha;Chen-Nee Chuah;Z. Ding
中科院分区：
文献类型：
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作者：
Taha Bouchoucha;Chen-Nee Chuah;Z. Ding
Learning and exploring the connectivity of unknown networks represent an important problem in practical applications of communication networks and social-media networks. Modeling large-scale networks as connected graphs is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This paper investigates a simple measurement model in which a small subset of source nodes collect hop distance information from networked nodes in order to generate a virtual coordinate system (VCS) for networks of unknown topology. We establish the VCS to define logical distance among nodes based on principal component analysis and to determine connectivity relationship and effective routing methods. More importantly, we present a robust analytical algorithm to derive the VCS against practical issues of missing and corrupted measurements. We also develop a connectivity inference method which classifies nodes into layers based on the hop distances and derives partial information on network connectivity.
