Post and repost: A holistic view of budgeted influence maximization

Abstract

Existing studies on influence maximization (IM) mainly focus on activating a set of influential users (seed nodes). Originated from the seed nodes’ promotion actions (e.g., posting an advertising tweet) on social networks, a large influence spread might be triggered. However, in practice it is usually very expensive to have influential users posting original tweets in a promotional event. In contrast, it will incur much lower costs to have influential users reposting tweets and have ordinary users posting original tweets. Inspired by these observations, in this paper, we consider the Holistic Budgeted Influence Maximization (HBIM) problem, which maximizes the influence spread by deploying the budget to select seed nodes (for posting) and boost nodes (for reposting). To tackle the NP-hardness and non-submodularity of the problem, we devise two efficient algorithms with the data-dependent approximation ratios. Extensive experiments on real social networks demonstrate the efficiency and effectiveness of our proposed algorithms.