Online influence maximization in the absence of network structure

In online influence maximization, a learner aims to find a specified number of nodes that have the greatest influence in a network, by iteratively selecting seed nodes (i.e., initially activated nodes) and updating its knowledge of the network via activation feedback. Existing approaches to this problem customarily assume that the structure of the network is known in advance, and focus on how to utilize activation feedback to reveal the features of seed nodes in each iteration, regardless of non-seed nodes which occupy the majority of the node set. In this paper, we present a novel learning framework to carry out online influence maximization in the absence of