Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.
Ghonge, S., & Vural, D. C. (2017). Inferring network structure from cascades. Physical Review E, 96(1). https://doi.org/10.1103/PhysRevE.96.012319