Influence maximization using combined community-level influence score

Influence maximization (IM) is a NP-hard problem in the field of complex network with diverse applications, including viral marketing, information diffusion, and epidemic modeling. The goal is to identify a small set of influential nodes capable of initiating large-scale diffusion cascades. Although numerous IM algorithms have been developed to improve computational efficiency and output accuracy, they often face a trade-off between scalability and precision. Community-based IM methods address this challenge by exploiting the modular structure of networks to reduce the search space and enable parallel computation. This study proposes a novel community-based approach, Influence Maximization using Combined Community-level Influence Score (IMCCIS), which integrates both intra-community (local-level) and inter-community (global-level) influence scores. These scores are combined through a heuristic weighting scheme to identify nodes that effectively propagate influence within and across communities. Additionally, a penalty mechanism is introduced to mitigate overlapping influence among selected nodes. The proposed algorithm is inherently parallelizable, enhancing its suitability for large-scale networks. Comprehensive experiments on multiple real-world datasets demonstrate that IMCCIS achieves superior propagation performance and lower execution time compared to contemporary heuristic and community-based IM methods.