  Automatic feature learning algorithms are at the forefront of modern day
machine learning research. We present a novel algorithm, supervised Q-walk,
which applies Q-learning to generate random walks on graphs such that the walks
prove to be useful for learning node features suitable for tackling with the
node classification problem. We present another novel algorithm, k-hops
neighborhood based confidence values learner, which learns confidence values of
labels for unlabelled nodes in the network without first learning the node
embedding. These confidence values aid in learning an apt reward function for
Q-learning.
  We demonstrate the efficacy of supervised Q-walk approach over existing
state-of-the-art random walk based node embedding learners in solving the
single / multi-label multi-class node classification problem using several real
world datasets.
  Summarising, our approach represents a novel state-of-the-art technique to
learn features, for nodes in networks, tailor-made for dealing with the node
classification problem.
