Deep Graph Representation Learning and Optimization for Influence Maximization

• URL: http://arxiv.org/abs/2305.02200v2
• Date: Sat, 6 May 2023 15:02:48 GMT
• Title: Deep Graph Representation Learning and Optimization for Influence Maximization
• Authors: Chen Ling, Junji Jiang, Junxiang Wang, My Thai, Lukas Xue, James Song, Meikang Qiu, Liang Zhao
• Abstract summary: In Influence (IM) is formulated as selecting a set of initial users from a social network to maximize the expected number of influenced users. We propose a novel framework DeepIM to generatively characterize the latent representation of seed sets. We also design a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints.
• Score: 10.90744025490539
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Influence maximization (IM) is formulated as selecting a set of initial users from 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 the past few years, learning-based IM methods have emerged to achieve stronger generalization ability to unknown graphs than traditional ones. However, the development of learning-based IM methods is still limited by fundamental obstacles, including 1) the difficulty of effectively solving the objective function; 2) the difficulty of characterizing the diversified underlying diffusion patterns; and 3) the difficulty of adapting the solution under various node-centrality-constrained IM variants. To cope with the above challenges, we design a novel framework DeepIM to generatively characterize the latent representation of seed sets, and we propose to learn the diversified information diffusion pattern in a data-driven and end-to-end manner. Finally, we design a novel objective function to infer optimal seed sets under flexible node-centrality-based budget constraints. Extensive analyses are conducted over both synthetic and real-world datasets to demonstrate the overall performance of DeepIM. The code and data are available at: https://github.com/triplej0079/DeepIM.

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