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

TL;DR

COEVOLVE is a novel joint point process model that captures the intertwined dynamics of information diffusion and network evolution, enabling better simulation and prediction of social network behaviors.

Contribution

This paper introduces COEVOLVE, the first model to jointly analyze and simulate the co-evolution of information diffusion and network topology changes.

Findings

Model accurately fits real-world Twitter data

Provides more precise predictions than existing methods

Efficiently simulates interleaved diffusion and network events

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 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.