  Identifying influential nodes in the complex networks is of theoretical and
practical significance. There are many methods are proposed to identify the
influential nodes in the complex networks. In this paper, a local structure
entropy which is based on the degree centrality and the statistical mechanics
is proposed to identifying the influential nodes in the complex network.
  In the definition of the local structure entropy, each node has a local
network, the local structure entropy of each node is equal to the structure
entropy of the local network. The main idea in the local structure entropy is
try to use the influence of the local network to replace the node's influence
on the whole network.
  The influential nodes which are identified by the local structure entropy are
the intermediate nodes in the network. The intermediate nodes which connect
those nodes with a big value of degree.
  We use the $Susceptible-Infective$ (SI) model to evaluate the performance of
the influential nodes which are identified by the local structure entropy. In
the SI model the nodes use as the source of infection. According to the SI
model, the bigger the percentage of the infective nodes in the network the
important the node to the whole networks. The simulation on four real networks
show that the proposed method is efficacious and rationality to identify the
influential nodes in the complex networks.
