Heuristic Algorithms for Influence Maximization in Partially Observable Social Networks.

We consider the problem of selecting the most influential members within a social network, in order to disseminate a message as widely as possible. This problem, also referred to as seed selection for influence maximization, has been under intensive investigation since the emergence of social networks. Nonetheless, a large body of existing research is based on the assumption that the network is completely known, whereas little work considers partially observable networks. Yet, due to many issues including the extremely large size of current networks and privacy considerations, assuming full knowledge of the network is rather unrealistic. Despite this, an influencer often wishes to distribute its message far beyond the boundaries of the known network. In this paper, we propose a set of novel heuristic algorithms that specifically target nodes at this boundary, in order to maximize influence across the whole network. We show that these algorithmsoutperform the state of the art by up to 38% in networks with partial observability.