# Unsupervised Instance and Subnetwork Selection for Network Data

arXiv: https://arxiv.org/abs/2212.12771 · HTML: https://mlsanity.com/paper/2212.12771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 24 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Unsupervised Feature Selection with Adaptive Structure Learning](https://mlsanity.com/paper/1504.00736/) (2015)
- [Temporal Graph Signal Decomposition](https://mlsanity.com/paper/2106.13517/) (2021)
- [A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1510.05058/) (2015)
- [DSL: Discriminative Subgraph Learning via Sparse Self-Representation](https://mlsanity.com/paper/1904.00791/) (2019)
- [Discriminative Subnetworks with Regularized Spectral Learning for Global-state Network Data](https://mlsanity.com/paper/1512.06173/) (2015)

## Abstract (arXiv)

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.