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 contribute to understanding 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. This paper proposes 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. It considers direct and indirect impacts together and constructs a recursive order interaction relationship strength influence matrix, characterizing the global impacts between nodes comprehensively, from local to global, static to dynamic. And comprehensive analysis of 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 performs better in sorting accuracy, effectiveness, and the ability of top-k node infection.