Efficient information diffusion in time-varying graphs through deep reinforcement learning

Abstract

Network seeding for efficient information diffusion over time-varying graphs (TVGs) is a challenging task with many real-world applications. There are several ways to model this spatio-temporal influence maximization problem, but the ultimate goal is to determine the best moment for a node to start the diffusion process. In this context, we propose Spatio-Temporal Influence Maximization (STIM), a model trained with Reinforcement Learning and Graph Embedding over a set of artificial TVGs that is capable of learning the temporal behavior and connectivity pattern of each node, allowing it to predict the best moment to start a diffusion through the TVG. We focus on the scenario where some nodes in the TVG present periodic connectivity patterns, an aspect that received little attention in previous approaches. We also develop a special set of artificial TVGs used for training that simulate a stochastic diffusion process in TVGs, showing that the STIM network can learn an efficient policy even over a non-deterministic environment. After trained, STIM can be used in TVGs of any size, since the number of parameters of the model is independent to the size of the TVG being processed. STIM is also evaluated in two real-world TVGs, where it also manages to efficiently propagate information through the nodes. Finally, we also show that the STIM model has a time complexity of O(|E|). STIM is also highly versatile, where one can change the goal of the model by simply changing the adopted reward function.