Summary of Online Graph Filtering Over Expanding Graphs, by Bishwadeep Das et al.
Online Graph Filtering Over Expanding Graphs
by Bishwadeep Das, Elvin Isufi
First submitted to arxiv on: 11 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 In this paper, researchers tackle the challenge of processing signals over graphs that evolve over time, which is common in many real-world networks. Conventional graph filters are ill-suited for this task because they assume a fixed number of nodes. To address this issue, the authors propose an online graph filtering framework that relies on online learning principles. The framework can handle scenarios where the topology is both known and unknown. A regret analysis is performed to evaluate the performance of different components, including the online algorithm, filter order, and growing graph model. Numerical experiments with synthetic and real data demonstrate the competitiveness of the proposed approach for graph signal inference tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to process signals over graphs that change over time. Graph filters are a common tool used in many areas, but they usually assume the number of nodes stays the same. In reality, networks often grow or shrink, making it hard to use these filters effectively. The researchers propose a new way to do online graph filtering, which is better suited for changing graphs. They tested their approach on both fake and real data and showed that it works well. |
Keywords
» Artificial intelligence » Inference » Online learning
