Influence maximization through exploring structural information

Influence maximization (IM) is a widely investigated issue in the study of social networks because of its potential commercial and social value. The purpose of IM is to identify a group of influential nodes that will spread information to other nodes in a network while simultaneously maximizing the number of nodes that are ultimately influenced. Traditional IM methods have different limitations, such as limited scalability to address large-scale networks and the neglect of community structural information. Here, we propose a novel influence maximization approach, i.e., the layered gravity bridge algorithm (LGB), to address the IM problem, which emphasizes the local structural information of networks and combines community detection algorithms with an improved gravity model. With the proposed LGB, a community detection method is used to derive the communities, and then the bridge nodes are found, which can be regarded as possible candidate seeds. Later, communities are merged into larger communities according to our proposed algorithm, and new bridge nodes are determined. Finally, all candidate seed nodes are sorted through an improved gravity model to determine the final seed nodes. The algorithm fully explores the network structural information provided by the communities, thereby making it superior to the current algorithms in terms of the number of ultimately infected nodes. Furthermore, our proposed algorithm possesses the potential to alleviate the influence overlap effect of seed nodes. To verify the effect of our approach, the classical SIR model is adopted to propagate information with the selected seed nodes, while experiments are performed on several practical datasets. As indicated by the obtained results, the performance of our proposed algorithm outperforms existing ones.