Summary of Online Network Inference From Graph-stationary Signals with Hidden Nodes, by Andrei Buciulea et al.
Online Network Inference from Graph-Stationary Signals with Hidden Nodes
by Andrei Buciulea, Madeline Navarro, Samuel Rey, Santiago Segarra, Antonio G. Marques
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel method for online graph estimation that tackles the challenge of hidden nodes, where not all information is available simultaneously or completely. The proposed approach, called “online graph estimation,” uses signals that are stationary on the underlying graph to model unknown connections to hidden nodes. The authors formulate a convex optimization problem and solve it using an efficient proximal gradient algorithm that can run in real-time as data arrives sequentially. Experimental results on synthetic and real-world data demonstrate the viability of this approach for online graph learning with missing observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding connections between things (like people or places) even when we don’t have all the information at once. Right now, most methods assume that all information is available and can be seen all at once. But in real life, this isn’t always true. The authors come up with a new way to find these connections using signals that are consistent on the underlying graph. They show that their method works well for finding hidden connections even when some information is missing. |
Keywords
» Artificial intelligence » Optimization
