Online network inference under dynamic cascade updates: A node-centric approach

Abstract

Network inference is the process of inferring the structure of the unknown underlying network, based on the observations of the propagations of different contagions through the network. All the existing works consider the setting in which the information of the different propagations is available to the computation at the beginning. We introduce the problem of online network inference when the propagation information is revealed dynamically in batches. We present a new greedy heuristic that is amenable for online extension and derive two online inference algorithms. We present extensive experimental results show the computational gains that the online algorithms provide without losing much on the accuracy of the inferences.