268 citations · 1.1k across the 58 of their papers we have counts for
33 papers · 1 filter
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…
Learning the Topology of a Simplicial Complex Using Noisy Simplicial Signals
Andrei Buciulea, Elvin Isufi, Geert Leus +1
Graphs are a fundamental tool for modeling the irregular (non-Euclidean) structure of complex data. However, they are inherently limited to representing pairwise relationships, mak…
Exact Bayesian Tracking of Dynamic Network Topologies
Victor M. Tenorio, Elvin Isufi, Geert Leus +1
Tracking the temporal evolution of network topologies is a fundamental challenge in social networks, epidemiology, and sensor systems, among others. This paper develops an exact Ba…
Topological Kalman Filtering on Cell Complexes
Chengen Liu, Rohan Money, Ting Gao +3
Inferring latent dynamics from multivariate time-series defined over topological cell complexes is crucial for capturing the complex, higher-order interactions inherent in real-wor…
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…
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…
Learning the Topology of a Simplicial Complex Using Simplicial Signals: A Greedy Approach
A. Buciulea, E. Isufi, G. Leus +1
Graphs are ubiquitous to model the irregular (non-Euclidean) structure of complex data, but they are limited to pairwise relationships and fail to model the complexities of the dat…
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…
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…
Learning graphs and simplicial complexes from data
Andrei Buciulea, Elvin Isufi, Geert Leus +1
Graphs are widely used to represent complex information and signal domains with irregular support. Typically, the underlying graph topology is unknown and must be estimated from th…
On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks
Zhan Gao, Amanda Prorok, Elvin Isufi
Analyzing the stability of graph neural networks (GNNs) under topological perturbations is key to understanding their transferability and the role of each architecture component. H…
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…
Learning Stable Graph Neural Networks via Spectral Regularization
Zhan Gao, Elvin Isufi
Stability of graph neural networks (GNNs) characterizes how GNNs react to graph perturbations and provides guarantees for architecture performance in noisy scenarios. This paper de…
Graph Filters for Signal Processing and Machine Learning on Graphs
Elvin Isufi, Fernando Gama, David I. Shuman +1
Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and mac…
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…
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…
Learning Stochastic Graph Neural Networks with Constrained Variance
Zhan Gao, Elvin Isufi
Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the exp…
Convolutional Filtering in Simplicial Complexes
Elvin Isufi, Maosheng Yang
This paper proposes convolutional filtering for data whose structure can be modeled by a simplicial complex (SC). SCs are mathematical tools that not only capture pairwise relation…
Simplicial Convolutional Filters
Maosheng Yang, Elvin Isufi, Michael T. Schaub +1
We study linear filters for processing signals supported on abstract topological spaces modeled as simplicial complexes, which may be interpreted as generalizations of graphs that…
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks
Luana Ruiz, Fernando Gama, Alejandro Ribeiro +1
Graph convolutional neural networks (GCNNs) learn compositional representations from network data by nesting linear graph convolutions into nonlinearities. In this work, we approac…
Node-Adaptive Regularization for Graph Signal Reconstruction
Maosheng Yang, Mario Coutino, Geert Leus +1
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, w…
Online Time-Varying Topology Identification via Prediction-Correction Algorithms
Alberto Natali, Mario Coutino, Elvin Isufi +1
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of th…
Graph-Adaptive Activation Functions for Graph Neural Networks
Bianca Iancu, Luana Ruiz, Alejandro Ribeiro +1
Activation functions are crucial in graph neural networks (GNNs) as they allow defining a nonlinear family of functions to capture the relationship between the input graph data and…
Stochastic Graph Neural Networks
Zhan Gao, Elvin Isufi, Alejandro Ribeiro
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. Current GNN arch…