268 citations · 1.1k across the 58 of their papers we have counts for
8 papers · 1 filter
Topological Signal Processing and Learning: Recent Advances and Future Challenges
Elvin Isufi, Geert Leus, Baltasar Beferull-Lozano +2
Developing methods to process irregularly structured data is crucial in applications like gene-regulatory, brain, power, and socioeconomic networks. Graphs have been the go-to alge…
Sparse Covariance Neural Networks
Andrea Cavallo, Zhan Gao, Elvin Isufi
Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix of input data to leverage correlation information as pairwise connections. They have achieved…
Spatiotemporal Covariance Neural Networks
Andrea Cavallo, Mohammad Sabbaqi, Elvin Isufi
Modeling spatiotemporal interactions in multivariate time series is key to their effective processing, but challenging because of their irregular and often unknown structure. Stati…
Online Learning Of Expanding Graphs
Samuel Rey, Bishwadeep Das, Elvin Isufi
This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are…
Tracking Network Dynamics using Probabilistic State-Space Models
Victor M. Tenorio, Elvin Isufi, Geert Leus +1
This paper introduces a probabilistic approach for tracking the dynamics of unweighted and directed graphs using state-space models (SSMs). Unlike conventional topology inference m…
Online Graph Filtering Over Expanding Graphs
Bishwadeep Das, Elvin Isufi
Graph filters are a staple tool for processing signals over graphs in a multitude of downstream tasks. However, they are commonly designed for graphs with a fixed number of nodes,…