1 research outputs found
Earned Benefit Maximization in Social Networks Under Budget Constraint
Given a social network with nonuniform selection cost of the users, the
problem of \textit{Budgeted Influence Maximization} (BIM in short) asks for
selecting a subset of the nodes within an allocated budget for initial
activation, such that due to the cascading effect, influence in the network is
maximized. In this paper, we study this problem with a variation, where a set
of nodes are designated as target nodes, each of them is assigned with a
benefit value, that can be earned by influencing them, and our goal is to
maximize the earned benefit by initially activating a set of nodes within the
budget. We call this problem as the \textsc{Earned Benefit Maximization
Problem}. First, we show that this problem is NP\mbox{-}Hard and the benefit
function is \textit{monotone}, \textit{sub\mbox{-}modular} under the
\textit{Independent Cascade Model} of diffusion. We propose an incremental
greedy strategy for this problem and show, with minor modification it gives
\mbox{-}factor approximation guarantee on the earned
benefit. Next, by exploiting the sub\mbox{-}modularity property of the benefit
function, we improve the efficiency of the proposed greedy algorithm. Then, we
propose a hop\mbox{-}based heuristic method, which works based on the
computation of the `expected earned benefit' of the effective neighbors
corresponding to the target nodes. Finally, we perform a series of extensive
experiments with four real\mbox{-}life, publicly available social network
datasets. From the experiments, we observe that the seed sets selected by the
proposed algorithms can achieve more benefit compared to many existing methods.
Particularly, the hop\mbox{-}based approach is found to be more efficient than
the other ones for solving this problem.Comment: 12 Pages, 16 Figures, Submitted to a Journa