  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.
