Pricing influential nodes in online social networks

In uential nodes with rich connections in online social networks (OSNs) are of great values to initiate marketing campaigns. However, the potential in uence spread that can be generated by these in fluential nodes is hidden behind the structures of OSNs, which are often held by OSN providers and unavailable to advertisers for privacy concerns. A social advertising model known as in uencer marketing is to have OSN providers offer and price candidate nodes for advertisers to purchase for seeding marketing campaigns. In this setting, a reasonable price profile for the candidate nodes should effectively re flect the expected in fluence gain they can bring in a marketing campaign. In this paper, we study the problem of pricing the in-fluential nodes based on their expected in uence spread to help advertisers select the initiators of marketing campaigns without the knowledge of OSN structures. We design a function characterizing the divergence between the price and the expected in fluence of the initiator sets. We formulate the problem to minimize the divergence and derive an optimal price profile. An advanced algorithm is developed to estimate the price profile with accuracy guarantees. Experiments with real OSN datasets show that our pricing algorithm can significantly outperform other baselines.