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Found 16 papers, 1 papers with code
Implicit Neural Representations and the Algebra of Complex Wavelets
Implicit neural representations (INRs) have arisen as useful methods for representing signals on Euclidean domains.
Signal Processing on Product Spaces
We establish a framework for signal processing on product spaces of simplicial and cellular complexes.
Windowed Fourier Analysis for Signal Processing on Graph Bundles
We consider the task of representing signals supported on graph bundles, which are generalizations of product graphs that allow for "twists" in the product structure.
Enhanced graph-learning schemes driven by similar distributions of motifs
Guided by this, we first assume that we have a reference graph that is related to the sought graph (in the sense of having similar motif densities) and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the network topology inference optimization problem.
On Local Distributions in Graph Signal Processing
Leveraging this, we are able to seamlessly compare graphs of different sizes and coming from different models, yielding results on the convergence of spectral densities, transferability of filters across arbitrary graphs, and continuity of graph signal properties with respect to the distribution of local substructures.
Signal Processing on Cell Complexes
The processing of signals supported on non-Euclidean domains has attracted large interest recently.
Hodgelets: Localized Spectral Representations of Flows on Simplicial Complexes
We first show that the Hodge Laplacian can be used in lieu of the graph Laplacian to construct a family of wavelets for higher-order signals on simplicial complexes.
Signal processing on simplicial complexes
Higher-order networks have so far been considered primarily in the context of studying the structure of complex systems, i. e., the higher-order or multi-way relations connecting the constituent entities.
An Impossibility Theorem for Node Embedding
With the increasing popularity of graph-based methods for dimensionality reduction and representation learning, node embedding functions have become important objects of study in the literature.
Sparse Partial Least Squares for Coarse Noisy Graph Alignment
Graph signal processing (GSP) provides a powerful framework for analyzing signals arising in a variety of domains.
Principled Simplicial Neural Networks for Trajectory Prediction
We consider the construction of neural network architectures for data on simplicial complexes.
Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
In the context of simplicial complexes, we specifically focus on signal processing using the Hodge Laplacian matrix, a multi-relational operator that leverages the special structure of simplicial complexes and generalizes desirable properties of the Laplacian matrix in graph signal processing.
Rank-One Measurements of Low-Rank PSD Matrices Have Small Feasible Sets
We study the role of the constraint set in determining the solution to low-rank, positive semidefinite (PSD) matrix sensing problems.
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
In contrast to common practice which denoises then clusters, our method is a unified, convex approach that performs both simultaneously.
Network topology change-point detection from graph signals with prior spectral signatures
We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem.
Network Topology Inference with Graphon Spectral Penalties
In particular, we consider the case where the graph was drawn from a graphon model, and we supplement our convex optimization problem with a provably-valid regularizer on the spectrum of the graph to be recovered.
