Summary of Online Learning Of Expanding Graphs, by Samuel Rey et al.
Online Learning Of Expanding Graphs
by Samuel Rey, Bishwadeep Das, Elvin Isufi
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 A novel online algorithm for inferring the topology of expanding graphs is proposed in this paper. The existing methods focus on learning connectivity within a fixed set of nodes, but this approach fails to account for the growth of the graph as new nodes join the network. To address this issue, an online algorithm based on projected proximal gradient descent is introduced, which recursively updates the sample covariance matrix and handles different types of node updates. The method is specialized in Gaussian Markov random field settings, where computational complexity and cumulative regret are analyzed. The effectiveness of the proposed approach is demonstrated through controlled experiments and real-world datasets from epidemic and financial networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem! When new nodes join a network, it’s hard to keep track of how they’re connected. Most methods don’t account for this growth. The researchers propose a new way to learn about these connections in real-time. It’s like playing a game where the rules change as you go along. They show that their method works well with controlled experiments and real-world data from networks like those used to track epidemics or financial transactions. |
Keywords
» Artificial intelligence » Gradient descent
