Maximizing Coreness Gain via Incentive Allocation

To maintain the functionality of complex networks, recent studies focus on optimizing structural stability through anchor operations. The anchored coreness problem, for instance, seeks to maximize the overall coreness of all nodes by anchoring a selected subset. However, existing approaches overlook the cost associated with node anchoring, a critical factor in budget-constrained scenarios. To address this, we introduce the problem of maximizing coreness gain via incentive allocation, which aims to determine the optimal allocation of incentives to nodes under a given budget and node-specific cost functions, thereby maximizing the network’s overall coreness. We propose a baseline algorithm, GreedyIA, and an efficient heuristic algorithm, Batch-GreedyIA, designed to identify key nodes and their optimal incentive allocations. Batch-GreedyIA improves the computational efficiency of GreedyIA by incorporating a batch processing strategy and a reuse technique. Extensive experiments on eight real-world datasets validate the effectiveness of our model in enhancing network stability under budget constraints and demonstrate the efficiency of the proposed algorithms.