arXiv Machine Learning
A dictionary learning framework for graphs via filters and optimal transport
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 28
On a linear fused Gromov-Wasserstein distance for graph structured data
arXiv:2203. 04711v2 Announce Type: replace Abstract: We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem.
arXiv Machine Learning
Aug 31
Optimal Transport for Network Comparison: A Review with Machine Learning Applications
The paper reviews the use of optimal transport for comparing undirected, unweighted graphs, focusing on three main distances: Wasserstein, Gromov-Wasserstein, and Bures-Wasserstein. It discusses closed-form solutions for the Wasserstein distance in one dimension, how transport plans identify influential nodes after perturbations, and derives spectral bounds for the Bures-Wasserstein distance to avoid full decompositions. The authors evaluate these distances on synthetic clustering data and a real-world time‑series network for anomaly detection.
arXiv Machine Learning
3d ago
Optimal Transport for Network Comparison: A Unified Review with New Spectral Bounds and Machine Learning Applications
arXiv:2608.27500v3 Announce Type: replace-cross Abstract: Network comparison using optimal transport is a growing area of research in network science. Unlike standard graph metrics, optimal transport...
arXiv Machine Learning
Jul 24
Filter Learning for Subgraphs: Algebras and Performance Risk Bounds
arXiv:2607. 21263v1 Announce Type: new Abstract: Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice.
arXiv Machine Learning
Jun 8
Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
arXiv:2505. 21285v5 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs.
Hugging Face Trending Papers
Jul 23
Filter Learning for Subgraphs: Algebras and Performance Risk Bounds
Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a systematic framework for subgraph filter learning (SFL), where subgraph-supported operators approximate ambient graph filters under partial observations.