A serious challenge when finding influential actors in real-world social networks is the lack
of knowledge about the structure of the underlying network. Current state-of-the-art methods
rely on hand-crafted sampling algorithms; these methods sample nodes and their
neighbours in a carefully constructed order and choose opinion leaders from this discovered
network to maximize influence spread in the (unknown) complete network. In this work, we
propose a reinforcement learning framework for network discovery that automatically learns …

Social network interventions are used across a wide variety of domains to disseminate
information or inspire changes in behavior; application areas range from substance abuse
[24], to microfinance adoption [1], to HIV prevention [27, 28]. Such processes are
computationally modelled via the influence maximization problem, where the goal is to
select a subset of nodes from the network to spread a message, such that the number of
people it eventually reaches is maximized. Several algorithmic approaches have been …