Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems

Abstract

Learning and exploring 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, it is highly desirable to extract their connectivity information among nodes to visualize network topology, disseminate data, and improve routing efficiency. This work 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 a VCS to define logical distance among nodes based on principal component analysis (PCA) and to determine connectivity relationship and effective routing methods. More importantly, we present robust analytical algorithm to derive 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.

Topology Inference of Networks utilizing Rooted Spanning Tree Embeddings

“…In settings where nodes can learn their hop distance to all other nodes in the network, estimates on network topologies can be inferred from the hop distances of a subset of nodes [3]. So far, no existing F2F network supports collection of hop distance from one node to all other nodes.…”

“…However, when embeddings based on breadth-first-search spanning trees are used for routing, every node can learn its hop distance to a subset 𝑆 of nodes from the logical coordinate of their neighbors. The algorithm of Bouchoucha et al [3] then enables inference of links between nodes in 𝑆.…”

Fast Topology Inference of Wireless Networks Based on Hawkes Process

“…With the widespread application of wireless networks, the importance of intelligent analysis of network behaviors is becoming increasingly prominent. In the analysis of networks behaviors, learning and reasoning about the connectivity of unknown networks is a fundamental problem [1]. Through topology inference, we can mine the connection relationships between nodes to achieve the visual network's topology, and further improve our knowledge of the observed networks.…”