Influence Maximization

Influence maximization (IM) focuses on identifying the most impactful nodes within a network to maximize the spread of information or influence, a computationally challenging problem with applications in marketing, public health, and combating misinformation. Current research emphasizes developing efficient algorithms, often leveraging graph neural networks and reinforcement learning, to overcome the computational complexity and address limitations of traditional greedy approaches, particularly in scenarios with dynamic budgets, competing influences, or incomplete network information. These advancements are improving the accuracy and speed of IM solutions, leading to more effective strategies in various real-world applications.