An Influence Maximization Method Based on Effective K-Core for Social Networks - Yang, Wenqi (IJRDO -Journal of Computer Science Engineering, 2021)
Neighbours usually play an important role in the measurements of node influence. The number of neigbhours to a node is called its degree, which is a frequently adopted centrality. In order to solve the problem that traditional degree-based influence maximization algorithms fail to identify effective neighbours, this paper proposes a K-core based social network influence maximization method named K-core algorithm (EKCA). The proposed method first introduces the concept of K-core. Then it calculates the core of nodes based on K-core decomposition. Last, it uses coreness instead of degree as a standard to select effective neighbours. The proposed method could describe the position of nodes in the network more accurately, and thus better for the influence maximization problem. Experiments on networks with various sizes show that the proposed method can select nodes that spread more influence than the degree-based influence maximization algorithms.