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20222026
most citedLearning Expanding Graphs for Signal Interpolation

1 citations · 1 across the 8 of their papers we have counts for

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6 papers · 1 filter

eess.SP2026

Tracking Dynamic Simplicial Complexes via Constrained State-Space Estimation

Varun Sarathchandran, Bishwadeep Das, Elvin Isufi +1

Simplicial complexes (SCs) extend graphs to represent higher-order interactions, but couple their simplex levels through the inclusion property. While static SC inference is an eme…

eess.SP2025

Learning Product Graphs from Two-dimensional Stationary Signals

Andrei Buciulea, Bishwadeep Das, Elvin Isufi +1

Graph learning aims to infer a network structure directly from observed data, enabling the analysis of complex dependencies in irregular domains. Traditional methods focus on scala…

eess.SP2025

Graph signal aware decomposition of dynamic networks via latent graphs

Bishwadeep Das, Andrei Buciulea, Antonio G. Marques +1

Dynamics on and of networks refer to changes in topology and node-associated signals, respectively and are pervasive in many socio-technological systems, including social, biologic…

eess.SP2023

Online Filtering over Expanding Graphs

Bishwadeep Das, Elvin Isufi

Data processing tasks over graphs couple the data residing over the nodes with the topology through graph signal processing tools. Graph filters are one such prominent tool, having…

eess.SP2022

Graph filtering over expanding graphs

Bishwadeep Das, Elvin Isufi

Our capacity to learn representations from data is related to our ability to design filters that can leverage their coupling with the underlying domain. Graph filters are one such…

eess.SP2022★ 1 cited

Learning Expanding Graphs for Signal Interpolation

Bishwadeep Das, Elvin Isufi

Performing signal processing over graphs requires knowledge of the underlying fixed topology. However, graphs often grow in size with new nodes appearing over time, whose connectiv…