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

Unsupervised Instance and Subnetwork Selection for Network Data