Identifying influential nodes in complex networks: Effective distance gravity model

The identification of important nodes in complex networks is an area exciting growth due to its applications across various disciplines like disease control, data mining and network system control. Many measures have been proposed date, but they are either based on the locality or global nature network. These typically use traditional Euclidean Distance , which only focuses local static geographic distance between ignores dynamic interaction real-world networks. Both information should be considered for purpose identifying influential . In order address this problem, we original novel gravity model with effective fusion multi-level processing. Our method able comprehensively consider networks, also utilizes incorporate information. Moreover, can help us mine hidden topological structure more accurate results. susceptible infected model, Kendall correlation coefficient eight existing methods utilized carry out simulations twelve different real