Influence maximization in multilayer social networks using transformer-based node embeddings and deep neural networks

Studies have shown that individuals frequently interact and influence other people within a society. Understanding and identifying influential individuals within a network is crucial for optimizing information diffusion. This challenge, known as influence maximization, has attracted significant attention, particularly in multilayer social networks where individuals participate across diverse contexts. Traditional approaches rely on heuristic or approximation algorithms, but their scalability and adaptability remain limited. In this paper, we propose a novel deep neural network architecture to predict and maximize influence in multilayer social networks. The framework utilizes a combination of node- and layer-specific feature embeddings, a transformer encoder for contextual feature integration, and multilayer perceptron (MLPs) for influence regression. The input comprises feature vectors representing nodes and layers, which are encoded and aggregated to approximate the influence of each node within its respective layer. A final aggregation step computes the total influence spread across layers, enabling efficient seed set selection of highly influential nodes. Our method yields favorable outcomes, effectively tackling challenges such as hardware resource requirements, scalability, and runtime performance. Empirical evaluations against state-of-the-art algorithms demonstrate the effectiveness of the proposed model in achieving superior influence spread with reduced computational overhead. This approach proposes new paths for influence maximization in large-scale, multilayer social networks.