  Finding influential users in social networks is a fundamental problem with
many possible useful applications. Viewing the social network as a graph, the
influence of a set of users can be measured by the number of neighbors located
within a given number of hops in the network, where each hop marks a step of
influence diffusion. In this paper, we reduce the problem of IM to a
budget-constrained d-hop dominating set problem (kdDSP). We propose a unified
machine learning (ML) framework, FastCover, to solve kdDSP by learning an
efficient greedy strategy in an unsupervised way. As one critical component of
the framework, we devise a novel graph neural network (GNN) architecture, graph
reversed attention network (GRAT), that captures the diffusion process among
neighbors. Unlike most heuristic algorithms and concurrent ML frameworks for
combinatorial optimization problems, FastCover determines the entire seed set
from the nodes' scores computed with only one forward propagation of the GNN
and has a time complexity quasi-linear in the graph size. Experiments on
synthetic graphs and real-world social networks demonstrate that FastCover
finds solutions with better or comparable quality rendered by the concurrent
algorithms while achieving a speedup of over 1000x.
