Information Diffusion Meets Invitation Mechanism

Shiqi Zhang, Jiachen Sun, Wenqing Lin, Xiaokui Xiao, Yiqian Huang, Bo Tang

Abstract: The dissemination of information is a complex process that plays a crucial role in real-world applications, especially when intertwined with friend invitations and their ensuing responses. Traditional diffusion models, however, often do not adequately capture this invitation-aware diffusion (IAD), rendering inferior results. These models typically focus on describing the social influence process, i.e., how a user is informed by friends, but tend to overlook the subsequent behavioral changes that invitations might precipitate. To this end, we present the Independent Cascade with Invitation (ICI) model, which incorporates both the social influence process and multi-stage behavior conversions in IAD. We validate our design through an empirical study on in-game IAD. Furthermore, we conduct extensive experiments to evaluate the effectiveness of our proposal against 6 state-of-the-art models on 6 real-world datasets. In particular, we demonstrate that our solution can outperform the best competitor by up to 5× in cascade estimation and 17.2% in diffusion prediction. We deploy our proposal in the seed selection and friend ranking scenarios of Tencent's online games, where it achieves improvements of up to 170% and 20.3%, respectively.