COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
This addresses the challenge of understanding dynamic interactions in social networks for researchers and practitioners, though it is incremental as it builds on existing point process models.
The paper tackles the problem of modeling the co-evolution of information diffusion and network topology in online social networks, proposing the COEVOLVE model that jointly simulates these processes and shows good fit and more accurate predictions on synthetic and Twitter data.
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 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 predominantly studied separately, ignoring their co-evolutionary dynamics. 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. This 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. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives.