Finding Influencers in Complex Networks: An Effective Deep Reinforcement Learning Approach

TL;DR

This paper introduces DREIM, a deep reinforcement learning framework combining graph neural networks, which significantly improves influence maximization in complex networks over traditional methods, with high efficiency and scalability.

Contribution

It presents a novel end-to-end deep reinforcement learning model that outperforms existing algorithms in influence maximization tasks on large networks.

Findings

DREIM surpasses state-of-the-art algorithms in solution quality.

DREIM demonstrates linear scalability with network size.

The model achieves superior performance on both synthetic and real-world networks.

Abstract

Maximizing influences in complex networks is a practically important but computationally challenging task for social network analysis, due to its NP- hard nature. Most current approximation or heuristic methods either require tremendous human design efforts or achieve unsatisfying balances between effectiveness and efficiency. Recent machine learning attempts only focus on speed but lack performance enhancement. In this paper, different from previous attempts, we propose an effective deep reinforcement learning model that achieves superior performances over traditional best influence maximization algorithms. Specifically, we design an end-to-end learning framework that combines graph neural network as the encoder and reinforcement learning as the decoder, named DREIM. Trough extensive training on small synthetic graphs, DREIM outperforms the state-of-the-art baseline methods on very large synthetic and real-world networks on solution quality, and we also empirically show its linear scalability with regard to the network size, which demonstrates its superiority in solving this problem.