From IM to PIM: Revolutionizing Influence Maximization with Personalized Seed Generation

The rapid growth of online social networks has created significant opportunities for large-scale information dissemination, where many users seek to maximize the visibility and influence of their information, bringing significant attention to the problem of influence maximization (IM). IM aims to identify a limited set of influential users (seed nodes) to maximize information spread. However, existing IM approaches typically provide a unified solution for all users under a fixed seed budget, without considering that different users in real-world scenarios have inherently different target diffusion ranges and seed budgets. Such omissions lead to inefficiency and resource waste, particularly when excessive seed budgets are allocated to users with low diffusion demands. To overcome this issue, we introduce Personalized Influence Maximization (PIM) as an extension of classical IM. Building upon this formulation, we propose the Adaptive Graph Influencer Generator (AGIG), which models seed set selection as a sequence generation task and employs a causal transformer to autoregressively generate personalized and cost-effective seed sets tailored to diverse demands of users. In particular, AGIG incorporates an enhanced dual-view influence encoder that models realistic scenarios where information can reach users without direct connections but with similar interests, thereby strengthening node representations for high-quality seed generation. For effective training, we construct the Propagation Pathways Sequence Dataset by simulating diffusion processes under multiple classical diffusion models and graph structures, enabling AGIG to learn diverse propagation patterns across varying diffusion settings. Extensive experiments demonstrate that AGIG effectively adapts to diverse personalized propagation requirements, achieving an average improvement of approximately 15% in influence spread and cost efficiency over strong baseline methods across multiple datasets and diffusion settings.