Multi-Task Learning for Influence Estimation and Maximization

Abstract

We address the problem of influence maximization when the social network is accompanied by diffusion cascades. In the literature, such information is used to compute influence probabilities, which is utilized by stochastic diffusion models in influence maximization. Motivated by the recent criticism on diffusion models and the galloping advancements in influence learning, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IMINFECTOR</small> (Influence Maximization with INFluencer vECTORs), a method that uses representations learned from diffusion cascades to perform model-independent influence maximization. The first part of our methodology is a multi-task neural network that learns embeddings of nodes that initiate cascades (influencer vectors) and embeddings of nodes that participate in them (susceptible vectors). The norm of an influencer vector captures a node’s aptitude to initiate lengthy cascades and is used to reduce the number of candidate seeds. The combination of influencer and susceptible vectors form the diffusion probabilities between nodes. These are used to reformulate the computation of the influence spread and propose a greedy solution to influence maximization that retains the theoretical guarantees. We apply our method in three sizable datasets and evaluate it using cascades from future time steps. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IMINFECTOR</small> ’s scalability and accuracy outperform various competitive algorithms and metrics from the diverse landscape of influence maximization.