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Influence maximization (IM) is formulated as selecting a set of initial usersfrom a social network to maximize the expected number of influenced users.Researchers have made great progress in designing various traditional methods,and their theoretical design and performance gain are close to a limit. In thepast few years, learning-based IM methods have emerged to achieve strongergeneralization ability to unknown graphs than traditional ones. However, thedevelopment of learning-based IM methods is still limited by fundamentalobstacles, including 1) the difficulty of effectively solving the objectivefunction; 2) the difficulty of characterizing the diversified underlyingdiffusion patterns; and 3) the difficulty of adapting the solution undervarious node-centrality-constrained IM variants. To cope with the abovechallenges, we design a novel framework DeepIM to generatively characterize thelatent representation of seed sets, and we propose to learn the diversifiedinformation diffusion pattern in a data-driven and end-to-end manner. Finally,we design a novel objective function to infer optimal seed sets under flexiblenode-centrality-based budget constraints. Extensive analyses are conducted overboth synthetic and real-world datasets to demonstrate the overall performanceof DeepIM. The code and data are available at:https://github.com/triplej0079/DeepIM.

Deep Graph Representation Learning and Optimization for Influence Maximization