Predicting Influence Probabilities using Graph Convolutional Networks

As one of the fundamental tasks in data analytics, Influence Maximization methods have been widely used in many real-world applications. For instance, in social network analysis, after building a directed graph, where edges are weighted with influence probabilities, influence maximization methods can be used to find a set of users who can maximize the spread of information under certain cascade models. Despite their successes, however, one critical weakness of existing influence maximization methods lies in the fact that edges are weighted with historical probabilities. As such, influence maximization methods perform sub-optimal if there occur non-trivial changes in future. In response to this challenge, in this work, we propose a novel prediction-driven influence maximization method that accurately predicts future influence probabilities using graph convolutional networks and find seed users based on the predicted probabilities. The experiments with five real-world datasets show that our prediction accuracy is accurate (e.g., mean absolute percentage error less than 0.1) in many cases, and our prediction-driven influence maximization is very close to the optimal.