  The task of determining labels of all network nodes based on the knowledge
about network structure and labels of some training subset of nodes is called
the within-network classification. It may happen that none of the labels of the
nodes is known and additionally there is no information about number of classes
to which nodes can be assigned. In such a case a subset of nodes has to be
selected for initial label acquisition. The question that arises is: "labels of
which nodes should be collected and used for learning in order to provide the
best classification accuracy for the whole network?". Active learning and
inference is a practical framework to study this problem.
  A set of methods for active learning and inference for within network
classification is proposed and validated. The utility score calculation for
each node based on network structure is the first step in the process. The
scores enable to rank the nodes. Based on the ranking, a set of nodes, for
which the labels are acquired, is selected (e.g. by taking top or bottom N from
the ranking). The new measure-neighbour methods proposed in the paper suggest
not obtaining labels of nodes from the ranking but rather acquiring labels of
their neighbours. The paper examines 29 distinct formulations of utility score
and selection methods reporting their impact on the results of two collective
classification algorithms: Iterative Classification Algorithm and Loopy Belief
Propagation.
  We advocate that the accuracy of presented methods depends on the structural
properties of the examined network. We claim that measure-neighbour methods
will work better than the regular methods for networks with higher clustering
coefficient and worse than regular methods for networks with low clustering
coefficient. According to our hypothesis, based on clustering coefficient we
are able to recommend appropriate active learning and inference method.
