Papers › Budgeted Influence and Earned Benefit Maximization with Tags in Social Networks
Budgeted Influence and Earned Benefit Maximization with Tags in Social Networks
Suman Banerjee, Bithika Pal
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Given a social network, where each user is associated with a selection cost, the problem of \textsc{Budgeted Influence Maximization} (\emph{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 \textsc{Tag\mbox{-}Based Budgeted Influence Maximization problem} (\emph{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 \textsc{Earned Benefit Maximization} Problem in tag specific influence probability setting, which formally we call the \textsc{Tag\mbox{-}Based Earned Benefit Maximization problem} (\emph{TEBM Problem} in short). For this problem along with the inputs of the TBIM Problem, we are given a subset of the nodes as target users, and each one of them is associated with a benefit value that can be earned by influencing them. Considering the fact that different tag has different popularity across the communities of the same network, we propose three methodologies that work based on \emph{effective marginal influence gain computation}. The proposed methodologies have been analyzed for their time and space requirements.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections