  Data mining practitioners are facing challenges from data with network
structure. In this paper, we address a specific class of global-state networks
which comprises of a set of network instances sharing a similar structure yet
having different values at local nodes. Each instance is associated with a
global state which indicates the occurrence of an event. The objective is to
uncover a small set of discriminative subnetworks that can optimally classify
global network values. Unlike most existing studies which explore an
exponential subnetwork space, we address this difficult problem by adopting a
space transformation approach. Specifically, we present an algorithm that
optimizes a constrained dual-objective function to learn a low-dimensional
subspace that is capable of discriminating networks labelled by different
global states, while reconciling with common network topology sharing across
instances. Our algorithm takes an appealing approach from spectral graph
learning and we show that the globally optimum solution can be achieved via
matrix eigen-decomposition.
