Jianjun Luo’s research while affiliated with Worcester Polytechnic Institute and other places

... In [14], the issue of maximizing multi-item influence in continuous settings is examined, taking into account situations in which various influencers are offered disparate incentives on various items to entice them to participate in the viral marketing process. The goal of the cluster greedy algorithm, which is covered in [15], is to maximize the influence by dividing the social network into clusters and choosing seed influencers in an effective manner by combining the basic greedy algorithm with an investigation of the sub modularity property of the diffusion function. In [16], a quantum computing strategy for influence maximization is examined with the goal of obtaining near-optimal solutions through the use of effective quadratic unconstrained binary optimization formulations on quantum annealer and the transformation of the influence maximization problem into a max-cover instance problem. ...

Reference:

Navigating Influencer Marketing: A Comprehensive Overview

... Motivated by the above application, opinion-aware influence maximization [4]- [8] (OIM) problems consider users' opin- ions towards specific items (e.g., products and events), which might be positive, neutral, or negative, and their goals are mostly to maximize the positive opinions while minimizing the negative opinions among all influenced users. Although OIM has been extensively studied in the literature, prior work still has several limitations. ...

Reference:

Opinion-aware Influence Maximization in Online Social Networks