Dynamic Gradient Influencing for Viral Marketing Using Graph Neural Networks
This work addresses viral marketing in social networks for marketers or businesses, offering an incremental improvement by applying GNNs to a known problem with specific gains.
The paper tackles the viral marketing problem by proposing a data-driven approach using Graph Neural Networks to model product adoption, aiming to minimize budget and achieve adoption goals through dynamic topological and attribute changes, resulting in average gains of 24% on budget and 37% on AUC compared to baselines.
The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynamic Viral Marketing (DVM) problem seeks to find the minimum budget and minimal set of dynamic topological and attribute changes in order to attain a specified adoption goal. We show that DVM is NP-Hard and is related to the existing influence maximization problem. Motivated by this connection, we develop the idea of Dynamic Gradient Influencing (DGI) that uses gradient ranking to find optimal perturbations and targets low-budget and high influence non-adopters in discrete steps. We use an efficient strategy for computing node budgets and develop the ''Meta-Influence'' heuristic for assessing a node's downstream influence. We evaluate DGI against multiple baselines and demonstrate gains on average of 24% on budget and 37% on AUC on real-world attributed networks. Our code is publicly available at https://github.com/saurabhsharma1993/dynamic_viral_marketing.