DeepSN: A Sheaf Neural Framework for Influence Maximization

Abstract Influence maximization is a key topic in data mining, with broad applications in social network analysis and viral marketing. In recent years, researchers have increasingly turned to machine learning techniques to address this problem. By learning the underlying diffusion processes from data, these methods improve the generalizability of solutions while optimizing objectives to identify the optimal seed set for maximizing influence. Nonetheless, two fundamental challenges remain unresolved: (1) While Graph Neural Networks (GNNs) are increasingly employed to learn diffusion models, their traditional architectures often fail to capture the complex dynamics of influence diffusion, (2) Designing optimization objectives is inherently difficult due to the combinatorial explosion associated with solving this problem. To address these challenges, we propose a novel framework, DeepSN. Our framework employs sheaf neural diffusion to learn diverse influence patterns in a data-driven, end-to-end manner, providing enhanced separability in capturing diffusion characteristics. We also propose an optimization technique that accounts for overlapping influence between vertices, significantly reducing the search space and facilitating the identification of the optimal seed set efficiently. Finally, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the effectiveness of our framework.