Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming Manuel Gomez Rodriguez, tenure-track research group director at the Max Planck Institute for Software Systems. He will be presenting work entitled COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution.  Please find the abstract below and on the website.

Manuel is pioneering an educational and research program at Max Planck in computational social sciences, and is very excited to learn from our experiences. Additionally, his work in machine learning, especially learning processes over networks, has received numerous awards recently. Those interested in meeting with Manuel are welcome to sign up here.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and lunch will be provided.  See you all there! To view previous Applied Statistics presentations, please visit the website.

-- Aaron Kaufman

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

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. In this talk, we introduce 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.