  We address the problem of influence maximization when the social network is
accompanied by diffusion cascades. In prior works, such information is used to
compute influence probabilities, which is utilized by stochastic diffusion
models in influence maximization. Motivated by the recent criticism on the
effectiveness of diffusion models as well as the galloping advancements in
influence learning, we propose IMINFECTOR (Influence Maximization with
INFluencer vECTORs), a unified approach that uses representations learned from
diffusion cascades to perform model-independent influence maximization that
scales in real-world datasets. 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 the ability of
the node to create lengthy cascades and is used to estimate the expected
influence spread and reduce the number of candidate seeds. In addition, the
combination of influencer and susceptible vectors form the diffusion
probabilities between nodes. These are used to reformulate the network as a
bipartite graph and propose a greedy solution to influence maximization that
retains the theoretical guarantees.We a pply our method in three sizable
networks with diffusion cascades and evaluate it using cascades from future
time steps. IMINFECTOR is able to scale in all of them and outperforms various
competitive algorithms and metrics from the diverse landscape of influence
maximization in terms of efficiency and seed set quality.
