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.
Unified deep learning for graph signals, simplifying existing models.
This article develops a statistical test for the null hypothesis of strict stationarity of a discrete time stochastic process in the frequency domain. When the null hypothesis is true, the second order cumulant spectrum is zero at all the discrete Fourier frequency pairs in the principal domain. The test uses a window …
Tutorials on signal processing on higher-order networks like simplicial complexes and hypergraphs.
Data-driven methods link graphon limits to random walks and spectral clustering.
GCNN research tackles graph data topology and prediction.
Proposes LSGP for better graph signal representation.
Algorithm learns graph ARMA processes for missing signal estimation.
Paper reviews multi-way graph signal processing for tensor data.
This paper reconstructs complex graph signals using kernel methods on manifolds.
Graph signal processing improves machine learning for network data.
New framework models graph signals as distribution-valued signals in Wasserstein space.
New method for testing directed graphs using surrogate data.
Paper shows spectral filters can transfer between different graphs discretizing the same space.
Localized signal representation on graph bundles using Fourier analysis.
Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalizat…
Proposes a Gaussian process for graph signals using adaptive spectral kernels.
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…
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…
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…
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 …
Graph Signal Processing improves stock market volatility forecasting.
Graph-based methods for signal processing have shown promise for the analysis of data exhibiting irregular structure, such as those found in social, transportation, and sensor networks. Yet, though these systems are often dynamic, state-of-the-art methods for signal processing on graphs ignore the dimension of time, tr…
We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective …
Graph classification improved using spectral features and wavelet filters.
We propose Gaussian processes for signals over graphs (GPG) using the apriori knowledge that the target vectors lie over a graph. We incorporate this information using a graph- Laplacian based regularization which enforces the target vectors to have a specific profile in terms of graph Fourier transform coeffcients, fo…
Paper introduces signal processing on cell complexes.
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…
Revises GNN neighborhood aggregation for more accurate node classification.
A method for predicting signals on graphs using Gaussian processes and optimal transport.
Graph signal processing detects hallucinations in large language models.
We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to per- form non-iterative denoising directly in the graph frequency domai…
Proposes a novel graph signal model using narrowband kernels.
Novel Haar-Laplacian for directed graphs enhances spectral graph applications.
Researchers create benchmarks to compare graph inference methods.
The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this …
This paper extends compositional data analysis using graph signal processing.
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode…
Discrete noise improves graph generation quality and speed.
Graph Beta Diffusion (GBD) generates graphs with mixed discrete and continuous components.
Algorithm estimates sparse signals from linear measurements, improving recovery guarantees.
New method for identifying graph shift operators using vertex-time autoregressive models.
Paper develops an online EM algorithm for graph signal inference from streaming data.
Classifies graph configuration spaces homeomorphic to manifolds.
This paper presents a bias-variance tradeoff of graph Laplacian regularizer, which is widely used in graph signal processing and semi-supervised learning tasks. The scaling law of the optimal regularization parameter is specified in terms of the spectral graph properties and a novel signal-to-noise ratio parameter, whi…
A guide to using low-pass graph filters for network data.
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,…
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 …
Proposes a novel graph learning framework for robust graph topology learning from graph signals.