Deep Graph Representation Learning and Optimization for Influence Maximization

TL;DR

This paper introduces DeepIM, a deep learning framework for influence maximization that learns diversified diffusion patterns and optimizes seed sets under various constraints, outperforming traditional methods.

Contribution

The paper proposes a novel deep generative framework DeepIM for influence maximization, addressing key challenges and enabling flexible, data-driven seed selection.

Findings

DeepIM outperforms traditional influence maximization methods.

The framework effectively models diversified diffusion patterns.

Results are validated on synthetic and real-world datasets.

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.