  Understanding what graph layout human prefer and why they prefer is
significant and challenging due to the highly complex visual perception and
cognition system in human brain. In this paper, we present the first machine
learning approach for predicting human preference for graph layouts.
  In general, the data sets with human preference labels are limited and
insufficient for training deep networks. To address this, we train our deep
learning model by employing the transfer learning method, e.g., exploiting the
quality metrics, such as shape-based metrics, edge crossing and stress, which
are shown to be correlated to human preference on graph layouts. Experimental
results using the ground truth human preference data sets show that our model
can successfully predict human preference for graph layouts. To our best
knowledge, this is the first approach for predicting qualitative evaluation of
graph layouts using human preference experiment data.
