Unsupervised Instance and Subnetwork Selection for Network Data
This work addresses the challenge of handling high-dimensional, noisy network data with outliers and lacking global labels, which is common in domains like bioinformatics and social networks.
The paper tackles the problem of jointly selecting discriminative subnetworks and representative instances from unlabeled network data, such as gene expression on protein interaction networks, without supervision. The proposed UISS method achieves up to 10% accuracy improvement over state-of-the-art baselines on real-world datasets.
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.