COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

Mehrdad Farajtabar, Manuel Gomez-Rodriguez, Yichen Wang, Shuang Li, Hongyuan Zha, Le Song

Abstract: Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when they are exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes---information diffusion and network evolution---have been typically studied separately, ignoring their co-evolutionary dynamics. In this work, we propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. The model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Moreover, we develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. Experiments in both synthetic data and real data gathered from Twitter show that our model provides a good fit to the data as well as more accurate predictions than alternatives.