  The numerical controllability transition makes the success of control can be
achieved by increasing the number of driver nodes to a certain point. Motivated
by the fact that the degree correlation has vast role in the dynamics on
networks, we study the impact of various degree correlations of different
networks on the controllability transition point and find that the transition
point depicts local maximum in sparse networks as degree correlation r around
0.1 and 0 in ER and SF networks respectively. With the increasing of average
degree, the local maximum disappear and the controllability transition cannot
be influenced by degree correlation and degree distribution in dense ER
networks. The results are supported by numerical simulations and provide more
details to estimate the minimal driver nodes in large networks.
