  Network node similarity measure has been paid particular attention in the
field of statistical physics. In this paper, we utilize the concept of
information and information loss to measure the node similarity. The whole
model is based on this idea that if two nodes are more similar than the others,
then the information loss of seeing them as the same is less. The present new
method has low algorithm complexity so that it can save much time and energy to
deal with the large scale real-world network. We illustrate the availability of
this approach based on two artificial examples and computer-generated networks
by comparing its accuracy with the other selected approaches. The above tests
demonstrate that the new method can provide more reasonable results consistent
with our human common judgment. The new similarity measure method is also
applied to predict the network evolution and predict unknown nodes'
attributions in the two application examples.
