The influence maximization problem seeks to identify a set of influential users in a social network to maximize the spread of information. While prior research has focused extensively on improving computational efficiency, it has largely overlooked fairness in information dissemination across different social groups. A widely adopted fairness criterion is the maximin objective, which aims to maximize the minimum influence received by any group. However, under this objective, the fairness-aware influence maximization problem is NP-hard even to approximate well. In this work, we consider randomized seed selection strategies for fairness-aware influence maximization to address this challenge. We introduce a noise-based smoothing technique to tackle the non-smoothness of the objective function and develop an approximate solution based on the stochastic Frank–Wolfe algorithm. For efficient and theoretically grounded gradient estimation, we leverage the reverse influence sampling method, which enables provable gradient approximation. To obtain a discrete solution, we apply swap rounding to the fractional output, resulting in a randomized seed set that achieves a \((1-1/e,2\epsilon)\) -approximation for monotone functions and a \((1/e,2\epsilon)\) -approximation for non-monotone functions, with probability at least \(1-\delta\) , where \(\epsilon\) and \(\delta\) are user-defined accuracy parameters. Although accurate gradient estimation typically requires a large number of samples and may incur a high computational cost, we further derive upper and lower bounds on the gradient estimates and demonstrate that under certain conditions, using fewer samples still preserves the theoretical approximation guarantee. We validate our approach on six real-world social network datasets, and the results demonstrate that our algorithm effectively balances fairness and influence spread while maintaining strong performance.

Social-media platforms provide citizens a new way to stay informed and offer marketers a shot at promoting their brand. In social advertising, information diversity can create a level playing field for competitive information dissemination. Team diversity is important because teams with a diverse composition tend to perform better over time. The former demands that all the social networks’ users should receive diverse information. The later requires teams to be diverse with respect to team members’ attributes. However, to our knowledge, not only the diversity of the information spreading in the social network but also the influential users’ attributes are never simultaneously considered in research. Therefore, we propose a novel information- and team-based dual-diversity maximization (ITDM) problem in this article. The dual-diversity focused by the ITDM problem can be cast as a combination of information diversity and team diversity. The goal of ITDM problem is to obtain a good strategy for building marketing teams composed of influential social networks’ users. To some extent, this problem is an extension of classical IM problem that aims at selecting some influential users to trigger large information spread in social networks. The main difference between them is that team composition is taken into consideration by ITDM problem. Given that the ITDM problem is challenging, an algorithm on the foundation of Shapley value and negative-cycle-detection is designed to address it. We experimentally demonstrate the effectiveness of our algorithm on several real-world datasets.

To provide a new perspective for understanding the dynamics of advertising campaigns in online influencer marketing (OIM), this article proposes the modified agent-based (MAB) model. As an extension of the existing agent-based model, it takes into account some important factors such as influencer avoidance and product competition and modifies some existing parameters to provide a more realistic approach for simulating marketing campaigns. Based on it, we define a novel set function named as dual-profit function. It is proven to be nonsubmodular and nonsupermodular. The dual-profit maximization (DPM) problem which regards dual-profit function as its objective function and the DPM algorithm used to address DPM problem is proposed. The influence of several parameters is evaluated through experiments conducted on real-world datasets.