Budgeted Influence Maximization with Tags in Social Networks

Given a social network, where each user is associated with a selection cost, the problem of Budgeted Influence Maximization (BIM Problem) asks to choose a subset of them (known as seed users) within the allocated budget whose initial activation leads to the maximum number of influenced nodes. In reality, the influence probability between two users depends upon the context (i.e., tags). However, existing studies on this problem do not consider the tag specific influence probability. To address this issue, in this paper we introduce the Tag-Based Budgeted Influence Maximization Problem (TBIM Problem), where along with the other inputs, a tag set (each of them is also associated with a selection cost) is given, each edge of the network has the tag specific influence probability, and here the goal is to select influential users as well as influential tags within the allocated budget to maximize the influence. Considering the fact that different tag has different popularity across the communities of the same network, we propose three methodologies that work based on effective marginal influence gain computation. The proposed methodologies have been analyzed for their time and space requirements. We evaluate the methodologies with three datasets, and observe, that these can select seed nodes and influential tags, which leads to more number of influenced nodes compared to the baseline methods.