# Joint Signal Recovery and Graph Learning from Incomplete Time-Series

arXiv: https://arxiv.org/abs/2312.16940 · HTML: https://mlsanity.com/paper/2312.16940/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [How to learn a graph from smooth signals](https://mlsanity.com/paper/1601.02513/) (2016)
- [A Unified Framework for Structured Graph Learning via Spectral Constraints](https://mlsanity.com/paper/1904.09792/) (2019)
- [Signal Recovery on Graphs: Variation Minimization](https://mlsanity.com/paper/1411.7414/) (2014)
- [Learning Undirected Graphs in Financial Markets](https://mlsanity.com/paper/2005.09958/) (2020)
- [Decentralized Bayesian Learning over Graphs](https://mlsanity.com/paper/1905.10466/) (2019)
- [Graph Learning from Data under Structural and Laplacian Constraints](https://mlsanity.com/paper/1611.05181/) (2016)
- [Semi-Blind Inference of Topologies and Dynamical Processes over Graphs](https://mlsanity.com/paper/1805.06095/) (2018)
- [Towards stationary time-vertex signal processing](https://mlsanity.com/paper/1606.06962/) (2016)

## Built on by (most cited first, 3 of 3)

- [Time-Varying Graph Learning for Data with Heavy-Tailed Distribution](https://mlsanity.com/paper/2501.00606/) (2024)
- [Learning Time-Varying Graphs from Incomplete Graph Signals](https://mlsanity.com/paper/2510.17903/) (2025)
- [Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models](https://mlsanity.com/paper/2602.15920/) (2026)

## Abstract (arXiv)

Learning a graph from data is the key to taking advantage of graph signal processing tools. Most of the conventional algorithms for graph learning require complete data statistics, which might not be available in some scenarios. In this work, we aim to learn a graph from incomplete time-series observations. From another viewpoint, we consider the problem of semi-blind recovery of time-varying graph signals where the underlying graph model is unknown. We propose an algorithm based on the method of block successive upperbound minimization (BSUM), for simultaneous inference of the signal and the graph from incomplete data. Simulation results on synthetic and real time-series demonstrate the performance of the proposed method for graph learning and signal recovery.