Identifying important nodes based on neighborhood multi order multi attribute in complex networks

Abstract Identifying influential nodes in complex networks is always an important research direction in network science because it may attribute to understand the function and structure of networks and controlling the propagation process. However, most extant studies tend to over-rely on the network topology, thus ignoring the dynamic information interactions within the network. In this paper, we propose a node importance identification algorithm based on complex network neighborhood multi order multi attribute(MOMA). Its core idea is to use the space location feature attributes and special topological structure feature attributes, considering direct and indirect impacts together and constructing recursive order interaction relationship strength influence matrix. Characterizing the global impacts between nodes in a comprehensive way, from local to global, static to dynamic. And comprehensive analyze the importance of the nodes. In order to validate the performance of the proposed method, we compare the algorithm with six competing algorithms in nine real networks. The experimental results show that MOMA has a better performance in terms of sorting accuracy, effectiveness, and the ability of top-k node infection.