Influential node detection of social networks based on network invulnerability

Abstract Detecting influential nodes is still a popular issue in social networks and many excellent detecting methods have been put forward. However, most of them aim to improve the accuracy and efficiency of the algorithm, but ignore invulnerability of networks. Based on essential factors of influence propagation (such as the location and neighborhood of source node, propagation rate) and network invulnerability, we propose a novel strategy to search the influential nodes in terms of the local topology and the global location. Two important indicators are node diffusion degree and node cohesion degree, which are used to increase the probability of influence diffusion and reduce the feasibility of network collapse. More specially, the loss of global efficiency and the loss of local efficiency are applied to evaluate the impact of the algorithm from the perspective of network invulnerability. The experimental results in the real networks show that our method achieves an excellent balance between detecting accuracy and network invulnerability. The detected influential nodes are the ones that have great influence and can resist certain damage and disturbance of the networks.