Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement Learning

TL;DR

This paper introduces STIM, a reinforcement learning-based model that learns to identify optimal times for initiating information diffusion in time-varying graphs, demonstrating effectiveness in both artificial and real-world scenarios.

Contribution

The paper presents a novel reinforcement learning approach with graph embedding for influence maximization in TVGs, capable of learning temporal patterns and adaptable to different goals.

Findings

STIM effectively predicts optimal diffusion start times in artificial TVGs.

The model generalizes well to real-world TVGs for information propagation.

STIM operates with a time complexity of O(|E|).

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 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. STIM is also evaluated with a real-world TVG, 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, therefore, presents a novel approach for efficient information diffusion in TVGs, being highly versatile, where one can change the goal of the model by simply changing the adopted reward function.