Abstract / truncated to 115 words (read the full abstract)

Extending the concepts of classical signal processing to graphs, a wide array of methods have come to the fore, including filtering, reconstruction, classification, and sampling. Existing approaches in graph signal processing consider a known and static topology, i.e., fixed number of nodes and a fixed edge support. Two types of tasks stand out, namely, topology inference, where the edge support along with their weights are estimated from signals; and data processing, where existing data and the known topology are used to perform different tasks. However, such tasks become quite challenging when the network size and support changes over time. Particularly, these challenges involve adapting to the changing topology, data distributions and dealing with unknown topological ... toggle 8 keywords

graph signal processing dynamic graphs expanding graphs unknown edge support stochastic attachment graph filter design topology decomposition latent graphs

Information

Author
Das, Bishwadeep
Institution
TU Delft
Supervisors
Publication Year
2025
Upload Date
March 12, 2025

First few pages / click to enlarge

The current layout is optimized for mobile phones. Page previews, thumbnails, and full abstracts will remain hidden until the browser window grows in width.

The current layout is optimized for tablet devices. Page previews and some thumbnails will remain hidden until the browser window grows in width.