Identifying Influential Nodes Using a Shell-Based Ranking and Filtering Method in Social Networks

The main goal in the influence maximization problem (IMP) is to find k minimum nodes with the highest influence spread on the social networks. Since IMP is NP-hard and is not possible to obtain the optimum results, it is considered by heuristic algorithms. Many strategies focus on the power of the influence spread of core nodes to find k influential nodes. Most of the core detection-based methods concentrate on nodes in the highest core and often give the same power for all nodes in the best core. However, some other nodes fairly have the potential to select as seed nodes in other less important cores, because these nodes can play an important role in the diffusion of information among the core nodes with other nodes. Given this fact, this article proposes a new shell-based ranking and filtering method, called shell-based ranking and filtering method (SRFM), for selecting influential seeds with the aim to maximize influence in the network. The proposed algorithm initially selects a set of nodes in different shells. Moreover, a set of the candidate nodes are created, and most of the periphery nodes are removed during a pruning approach to reduce the computational overhead. Therefore, the seed nodes are selected from the candidate nodes set using the role of the bridge nodes. Experimental results in both synthetic and real data sets showed that the proposed SRFM algorithm has more acceptable efficiency in the influence spread and runtime than other algorithms.