  Influence maximization problem involves selecting a subset of seed nodes
within a social network to maximize information spread under a given diffusion
model, so how to identify the important nodes is the problem to be considered
in this paper. Due to the great differences in the reality of the network, a
class of multi-attribute decision fusion methods is often used to solve this
problem. Electre is mostly used to solve the problems of investment order,
benefit, and risk assessment of projects in economics, which supports the
decision maker to make choices by comparing the differences between a set of
alternatives. In this paper, we propose a multi-attribute decision fusion
method named SK-E, which construct local and global metrics for different
networks, use the improved Electre to make decision fusion between local and
global metrics of nodes, to get the optimal weight between local and global
metrics, and then identify the important nodes. The proposed method
demonstrates superior accuracy compared to other methods, as evaluated through
three experiments: the SIR epidemic model, the independent cascade model, and
constraint efficiency. These experiments were conducted across six different
real networks selected as the experimental dataset.
