Team Composition for Competitive Information Spread: Dual-Diversity Maximization Based on Information and Team

Abstract

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.