  Unlike tabular data, features in network data are interconnected within a
domain-specific graph. Examples of this setting include gene expression
overlaid on a protein interaction network (PPI) and user opinions in a social
network. Network data is typically high-dimensional (large number of nodes) and
often contains outlier snapshot instances and noise. In addition, it is often
non-trivial and time-consuming to annotate instances with global labels (e.g.,
disease or normal). How can we jointly select discriminative subnetworks and
representative instances for network data without supervision? We address these
challenges within an unsupervised framework for joint subnetwork and instance
selection in network data, called UISS, via a convex self-representation
objective. Given an unlabeled network dataset, UISS identifies representative
instances while ignoring outliers. It outperforms state-of-the-art baselines on
both discriminative subnetwork selection and representative instance selection,
achieving up to 10% accuracy improvement on all real-world data sets we use for
evaluation. When employed for exploratory analysis in RNA-seq network samples
from multiple studies it produces interpretable and informative summaries.
