  In many real-world networks, nodes have class labels, attributes, or
variables that affect the network's topology. If the topology of the network is
known but the labels of the nodes are hidden, we would like to select a small
subset of nodes such that, if we knew their labels, we could accurately predict
the labels of all the other nodes. We develop an active learning algorithm for
this problem which uses information-theoretic techniques to choose which nodes
to explore. We test our algorithm on networks from three different domains: a
social network, a network of English words that appear adjacently in a novel,
and a marine food web. Our algorithm makes no initial assumptions about how the
groups connect, and performs well even when faced with quite general types of
network structure. In particular, we do not assume that nodes of the same class
are more likely to be connected to each other---only that they connect to the
rest of the network in similar ways.
