COEVOLVE: A Joint Point Process Model for Information Diffusion and Network 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. 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 such as Twitter. 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. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

Authors

• Mehrdad Farajtabar
• Yichen Wang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering, Huazhong University of Science and Technology
• Manuel Gomez-Rodriguez
• Shuang Li Fudan University
• Hongyuan Zha Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong, Shenzhen
• Le Song

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