Influence maximization in social networks using transfer learning via graph-based LSTM

Social networks have emerged as efficient platforms to connect people worldwide and facilitate the rapid spread of information. Identifying influential nodes in social networks to accelerate the spread of particular information across the network formulates the study of influence maximization (IM). In this paper, inspired by deep learning techniques, we propose a novel approach to solve the influence maximization problem as a classical regression task using transfer learning via graph-based long short-term memory (GLSTM). We start by calculating three popular node centrality methods as feature vectors for the nodes in the network and every node's individual influence under susceptible-infected-recovered (SIR) information diffusion model, which forms the labels of the nodes in the network. The generated feature vectors and their corresponding labels for all the nodes are then fed into a graph-based long short-term memory (GLSTM). The proposed architecture is trained on a vast and complex network to generalize the model parameters better. The trained model is then used to predict the probable influence of every node in the target network. The proposed model is compared with some of the well-known and recently proposed algorithms of influence maximization on several real-life networks using the popular SIR model of information diffusion. The intensive experiments suggest that the proposed model outperforms these well-known and recently proposed influence maximization algorithms.