CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks
This work addresses the challenge of minimizing costly environment interactions in reinforcement learning for influence maximization, which is crucial for applications like HIV prevention and micro-finance adoption in physical social networks, but it appears incremental as it builds on existing RL frameworks.
The paper tackles the problem of costly network discovery for influence maximization in unknown real-world social networks by proposing a curriculum learning policy to improve the sample efficiency of reinforcement learning methods, showing that it outperforms the current best approach in experiments on real-world datasets.
Influence maximization is the problem of finding a small subset of nodes in a network that can maximize the diffusion of information. Recently, it has also found application in HIV prevention, substance abuse prevention, micro-finance adoption, etc., where the goal is to identify the set of peer leaders in a real-world physical social network who can disseminate information to a large group of people. Unlike online social networks, real-world networks are not completely known, and collecting information about the network is costly as it involves surveying multiple people. In this paper, we focus on this problem of network discovery for influence maximization. The existing work in this direction proposes a reinforcement learning framework. As the environment interactions in real-world settings are costly, so it is important for the reinforcement learning algorithms to have minimum possible environment interactions, i.e, to be sample efficient. In this work, we propose CLAIM - Curriculum LeArning Policy for Influence Maximization to improve the sample efficiency of RL methods. We conduct experiments on real-world datasets and show that our approach can outperform the current best approach.