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.
New graph coarsening method preserves GNN message-passing signals.
RP-GFRFT unifies fractional order and rotation control for graph signals.
New method for testing directed graphs using surrogate data.
Graphon pooling preserves spectral properties in GNNs, reducing overfitting.
Novel Haar-Laplacian for directed graphs enhances spectral graph applications.
A new aggregation strategy improves GNN performance and learning dynamics.
New method interprets ranked data on permutahedron graph.
COPT optimizes graph distances via simultaneous optimal transport.
We propose a novel framework for graph mean computation.
In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network interactions. Their efficient processing calls for dimensionality reduction techniques capable of p…
Proposes a novel graph signal model using narrowband kernels.
This paper reconstructs complex graph signals using kernel methods on manifolds.
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…
Deep Gaussian Processes model functions on DAGs with partially observed data.
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…
Localized signal representation on graph bundles using Fourier analysis.
Proposes a novel graph learning framework for robust graph topology learning from graph signals.
Graph-Dictionary model for sparse multivariate signal representation.
Paper develops an online EM algorithm for graph signal inference from streaming data.
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 …
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…
Novel framework improves graph learning for out-of-distribution generalization.
Detects graph topology changes from noisy signals using prior spectral information.
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…
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,…
Algorithm learns graph ARMA processes for missing signal estimation.
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…
Paper reviews multi-way graph signal processing for tensor data.
Proposes LSGP for better graph signal representation.
The paper detects changes in graph signal means offline.
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…
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 …
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 …
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 …
In this paper we consider the problem of graph-based transductive classification, and we are particularly interested in the directed graph scenario which is a natural form for many real world applications. Different from existing research efforts that either only deal with undirected graphs or circumvent directionality…
This paper explains GNNs using graph signal denoising.
This study improves graph coarsening methods by preserving graph spectrum and distances.
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…
We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured b…
The paper introduces a sampling theory for graphons with a Poincaré inequality and proves consistency.
Unsupervised dimension selection is an important problem that seeks to reduce dimensionality of data, while preserving the most useful characteristics. While dimensionality reduction is commonly utilized to construct low-dimensional embeddings, they produce feature spaces that are hard to interpret. Further, in applica…
The paper infers multiple graphs from stationary signals on them.
Eigen-GNN enhances GNNs by preserving graph structures.
DAGgr aggregates multiple DAGs to stabilize causal structure learning.
Spectral graph sparsification preserves geometry of GNN embeddings.