Online Directed Graph Estimation for Dynamic Network Topology Inference

Network topology inference based on collected observations plays a fundamental role in many smart applications such as internet of vehicles. However, identifying the variation of network topology may be challenged since the network may be directed, and the topology inference problem is usually modelled as a non-convex problem. Moreover, large amount of data may be required to correctly identify the change of network topology. In this paper, the network topology is characterized by directed graphs, and the estimation of topology variation is modelled as a convex problem based on graph filtering. Since signals filtering over graphs can be considered to be a model-based problem, much less observations are required, and the topology estimation can then be performed with online behaviours. The proposed algorithms are validated on synthetic and real datasets, and both of them demonstrate good estimation performances.