Budgeted influence and earned benefit maximization with tags in social networks

In case of Budgeted Influence Maximization (BIM), the users of the network have a cost and influential user selection needs to be done within a given budget. In case of Earned Benefit Maximization (EBM) Problem, a set of target users along with their benefit value is given and the aim is to choose highly influential users within an allocated budget to maximize the earned benefit.

In this paper, we study the BIM and EBM Problem under the tag-specific edge probability setting, which means instead of a single edge probability a set of probability values (each one for a specific context e.g., 'games,' 'academics,' etc.) per edge is given. The aim is to identify the influential tags and users for maximizing the influence and earned benefit. Considering the realistic fact that different tags have a different impact on different communities of a social network, we propose two solution methodologies and one pruning technique.