  The identification of important nodes in complex networks is an area of
exciting growth due to its applications across various disciplines like disease
controlling, community finding, data mining, network system controlling, just
to name a few. Many measures have thus been proposed to date, and these
measures are either based on the locality of nodes or the global nature of the
network. These measures typically use distance based on the concept of
traditional Euclidean Distance, which only focus on the local static geographic
distance between nodes but ignore the interaction between nodes in real-world
networks. However, a variety of factors should be considered for the purpose of
identifying influential nodes, such as degree, edge, direction and weight. Some
methods based on evidence theory have also been proposed. In this paper, we
have proposed an original and novel gravity model with effective distance for
identifying influential nodes based on information fusion and multi-level
processing. Our method is able to comprehensively consider the global and local
information of the complex network, and also utilize the effective distance to
replace the Euclidean Distance. This allows us to fully consider the complex
topological structure of the real network, as well as the dynamic interaction
information between nodes. In order to validate the effectiveness of our
proposed method, we have utilized the susceptible infected (SI) model to carry
out a variety of simulations on eight different real-world networks using six
existing well-known methods. The experimental results indicate the
reasonableness and effectiveness of our proposed method.
