A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a novel graph signal model using narrowband kernels.
New framework models graph signals as distribution-valued signals in Wasserstein space.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
Graph-Dictionary model for sparse multivariate signal representation.
Proposes a novel graph learning framework for robust graph topology learning from graph signals.
Paper develops an online EM algorithm for graph signal inference from streaming data.
Proposes LSGP for better graph signal representation.
Generative networks have made it possible to generate meaningful signals such as images and texts from simple noise. Recently, generative methods based on GAN and VAE were developed for graphs and graph signals. However, the mathematical properties of these methods are unclear, and training good generative models is di…
The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not always readily available from the data, nor easy to define depending…
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter…
Algorithm learns graph ARMA processes for missing signal estimation.
Modern data introduces new challenges to classic signal processing approaches, leading to a growing interest in the field of graph signal processing. A powerful and well established model for real world signals in various domains is sparse representation over a dictionary, combined with the ability to train the diction…
Graphs are a central tool in machine learning and information processing as they allow to conveniently capture the structure of complex datasets. In this context, it is of high importance to develop flexible models of signals defined over graphs or networks. In this paper, we generalize the traditional concept of wide …
Detects graph topology changes from noisy signals using prior spectral information.
The paper detects changes in graph signal means offline.
Proposes a new graph trend filtering model for inhomogeneous graph signals.
This paper reconstructs complex graph signals using kernel methods on manifolds.
Graph signals offer a very generic and natural representation for data that lives on networks or irregular structures. The actual data structure is however often unknown a priori but can sometimes be estimated from the knowledge of the application domain. If this is not possible, the data structure has to be inferred f…
This paper explains GNNs using graph signal denoising.
Graph Signal Processing improves stock market volatility forecasting.
Convolutional Neural Networks are very efficient at processing signals defined on a discrete Euclidean space (such as images). However, as they can not be used on signals defined on an arbitrary graph, other models have emerged, aiming to extend its properties. We propose to review some of the major deep learning model…
The paper infers multiple graphs from stationary signals on them.
Graph signal processing improves machine learning for network data.
Proposes a Gaussian process for graph signals using adaptive spectral kernels.
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on weighted graphs, an additional design challenge is to incorporate …
Localized signal representation on graph bundles using Fourier analysis.
LogSpecT learns graphs from stationary signals without infeasibility issues.
New method for testing directed graphs using surrogate data.
Graph signal processing detects hallucinations in large language models.
The paper introduces a sampling theory for graphons with a Poincaré inequality and proves consistency.
A number of applications in engineering, social sciences, physics, and biology involve inference over networks. In this context, graph signals are widely encountered as descriptors of vertex attributes or features in graph-structured data. Estimating such signals in all vertices given noisy observations of their values…
It has been shown recently that graph signals with small total variation can be accurately recovered from only few samples if the sampling set satisfies a certain condition, referred to as the network nullspace property. Based on this recovery condition, we propose a sampling strategy for smooth graph signals based on …
MultiImport infers node importance from multiple KG signals.
Uncertainty principles such as Heisenberg's provide limits on the time-frequency concentration of a signal, and constitute an important theoretical tool for designing and evaluating linear signal transforms. Generalizations of such principles to the graph setting can inform dictionary design for graph signals, lead to …
Graph learning method improves brain state classification.
Paper proposes a method to improve graph clustering by integrating node textual metadata with node signals in GGMs.
Revises GNN neighborhood aggregation for more accurate node classification.
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions,…
Graph-based framework predicts ADR signals from clinical data.
Graph classification improved using spectral features and wavelet filters.
Proposes GFMMD for comparing signals on graphs.
Paper reviews multi-way graph signal processing for tensor data.
Sampling is a fundamental topic in graph signal processing, having found applications in estimation, clustering, and video compression. In contrast to traditional signal processing, the irregularity of the signal domain makes selecting a sampling set non-trivial and hard to analyze. Indeed, though conditions for graph …
BankGCN improves graph convolution networks by handling multi-channel signals with adaptive filter banks.
Many signals on Cartesian product graphs appear in the real world, such as digital images, sensor observation time series, and movie ratings on Netflix. These signals are "multi-dimensional" and have directional characteristics along each factor graph. However, the existing graph Fourier transform does not distinguish …
Unified framework infers time-varying graphs from incomplete signals.
This work aims at recovering signals that are sparse on graphs. Compressed sensing offers techniques for signal recovery from a few linear measurements and graph Fourier analysis provides a signal representation on graph. In this paper, we leverage these two frameworks to introduce a new Lasso recovery algorithm on gra…
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is s…