Budgeted Influence Maximization with Tags in Social Networks

Abstract

Given a social network, where each user is associated with a selection cost, the problem of Budgeted Influence Maximization (BIM Problem in short) asks to choose a subset of them (known as seed users) within an allocated budget whose initial activation leads to the maximum number of influenced nodes.Existing Studies on this problem do not consider the tag-specific influence probability. However, in reality, influence probability between two users always depends upon the context (e.g., sports, politics, etc.). To address this issue, in this paper we introduce the Tag-Based Budgeted Influence Maximization problem (TBIM Problem in short), 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 real-world campaigns targeted in nature, we also study the Earned Benefit Maximization Problem in tag specific influence probability setting, which formally we call the Tag-Based $ A part of this paper has been previously published as Banerjee et al. [2020c]