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Tag: graph

  • Conformal Graph Prediction with Z-Gromov Wasserstein Distances

    Conformal Graph Prediction with Z-Gromov Wasserstein Distances arXiv:2603.02460v1 Announce Type: new Abstract: Supervised graph prediction addresses regression problems where the outputs are structured graphs. Although several approaches exist for graph–valued prediction, principled uncertainty quantification remains limited. We propose a conformal prediction framework for graph-valued outputs, providing distribution–free coverage guarantees in structured output spaces. Our method…

  • Graph Coloring You Can See

    Graph Coloring You Can See Visual intuition with Python The post Graph Coloring You Can See appeared first on Towards Data Science. Rhyd Lewis Go to original source

  • Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables

    Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables arXiv:2602.23611v1 Announce Type: new Abstract: Algorithmic decisions about individuals require predictions that are not only accurate but also fair with respect to sensitive attributes such as gender and race. Causal notions of fairness align with legal requirements, yet many…

  • Semi-Supervised Learning on Graphs using Graph Neural Networks

    Semi-Supervised Learning on Graphs using Graph Neural Networks arXiv:2602.17115v1 Announce Type: new Abstract: Graph neural networks (GNNs) work remarkably well in semi-supervised node regression, yet a rigorous theory explaining when and why they succeed remains lacking. To address this gap, we study an aggregate-and-readout model that encompasses several common message passing architectures: node features are…

  • Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models

    Including Node Textual Metadata in Laplacian-constrained Gaussian Graphical Models arXiv:2602.15920v1 Announce Type: new Abstract: This paper addresses graph learning in Gaussian Graphical Models (GGMs). In this context, data matrices often come with auxiliary metadata (e.g., textual descriptions associated with each node) that is usually ignored in traditional graph estimation processes. To fill this gap, we…

  • What Is a Knowledge Graph — and Why It Matters

    What Is a Knowledge Graph — and Why It Matters How structured knowledge became healthcare’s quiet advantage The post What Is a Knowledge Graph — and Why It Matters appeared first on Towards Data Science. Steve Hedden Go to original source

  • STARK denoises spatial transcriptomics images via adaptive regularization

    STARK denoises spatial transcriptomics images via adaptive regularization arXiv:2512.10994v1 Announce Type: new Abstract: We present an approach to denoising spatial transcriptomics images that is particularly effective for uncovering cell identities in the regime of ultra-low sequencing depths, and also allows for interpolation of gene expression. The method — Spatial Transcriptomics via Adaptive Regularization and Kernels…

  • Statistical-computational gap in multiple Gaussian graph alignment

    Statistical-computational gap in multiple Gaussian graph alignment arXiv:2512.00610v1 Announce Type: new Abstract: We investigate the existence of a statistical-computational gap in multiple Gaussian graph alignment. We first generalize a previously established informational threshold from Vassaux and Massouli’e (2025) to regimes where the number of observed graphs $p$ may also grow with the number of nodes…

  • How To Build a Graph-Based Recommendation Engine Using EDG and Neo4j

    How To Build a Graph-Based Recommendation Engine Using EDG and Neo4j Use a shared taxonomy to connect RDF and property graphs—and power smarter recommendations with inferencing The post How To Build a Graph-Based Recommendation Engine Using EDG and Neo4j appeared first on Towards Data Science. Steve Hedden Go to original source

  • Angular Graph Fractional Fourier Transform: Theory and Application

    Angular Graph Fractional Fourier Transform: Theory and Application arXiv:2511.16111v1 Announce Type: new Abstract: Graph spectral representations are fundamental in graph signal processing, offering a rigorous framework for analyzing and processing graph-structured data. The graph fractional Fourier transform (GFRFT) extends the classical graph Fourier transform (GFT) with a fractional-order parameter, enabling flexible spectral analysis while preserving…

  • Graph RAG vs SQL RAG

    Graph RAG vs SQL RAG Evaluating RAGs on graph and SQL databases The post Graph RAG vs SQL RAG appeared first on Towards Data Science. Reinhard Sellmair Go to original source

  • A Short Note on Upper Bounds for Graph Neural Operator Convergence Rate

    A Short Note on Upper Bounds for Graph Neural Operator Convergence Rate arXiv:2510.20954v1 Announce Type: new Abstract: Graphons, as limits of graph sequences, provide a framework for analyzing the asymptotic behavior of graph neural operators. Spectral convergence of sampled graphs to graphons yields operator-level convergence rates, enabling transferability analyses of GNNs. This note summarizes known…

  • Learning Time-Varying Graphs from Incomplete Graph Signals

    Learning Time-Varying Graphs from Incomplete Graph Signals arXiv:2510.17903v1 Announce Type: new Abstract: This paper tackles the challenging problem of jointly inferring time-varying network topologies and imputing missing data from partially observed graph signals. We propose a unified non-convex optimization framework to simultaneously recover a sequence of graph Laplacian matrices while reconstructing the unobserved signal entries.…

  • Surrogate Graph Partitioning for Spatial Prediction

    Surrogate Graph Partitioning for Spatial Prediction arXiv:2510.07832v1 Announce Type: new Abstract: Spatial prediction refers to the estimation of unobserved values from spatially distributed observations. Although recent advances have improved the capacity to model diverse observation types, adoption in practice remains limited in industries that demand interpretability. To mitigate this gap, surrogate models that explain black-box…

  • Eulerian Melodies: Graph Algorithms for Music Composition

    Eulerian Melodies: Graph Algorithms for Music Composition Conceptual overview and an end-to-end Python implementation The post Eulerian Melodies: Graph Algorithms for Music Composition appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • Why does your graph neural network fail on some graphs? Insights from exact generalisation error

    Why does your graph neural network fail on some graphs? Insights from exact generalisation error arXiv:2509.10337v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are widely used in learning on graph-structured data, yet a principled understanding of why they succeed or fail remains elusive. While prior works have examined architectural limitations such as over-smoothing and…

  • No Peeking Ahead: Time-Aware Graph Fraud Detection

    No Peeking Ahead: Time-Aware Graph Fraud Detection How to implement leak-free graph fraud detection The post No Peeking Ahead: Time-Aware Graph Fraud Detection appeared first on Towards Data Science. Erika G. Gonçalves Go to original source

  • Graph Coloring for Data Science: A Comprehensive Guide

    Graph Coloring for Data Science: A Comprehensive Guide From theoretical puzzles to practical applications The post Graph Coloring for Data Science: A Comprehensive Guide appeared first on Towards Data Science. Chinmay Kakatkar Go to original source

  • GraphPPD: Posterior Predictive Modelling for Graph-Level Inference

    GraphPPD: Posterior Predictive Modelling for Graph-Level Inference arXiv:2508.16995v1 Announce Type: new Abstract: Accurate modelling and quantification of predictive uncertainty is crucial in deep learning since it allows a model to make safer decisions when the data is ambiguous and facilitates the users’ understanding of the model’s confidence in its predictions. Along with the tremendously increasing…

  • A pseudo-inverse of a line graph

    A pseudo-inverse of a line graph arXiv:2508.09412v1 Announce Type: new Abstract: Line graphs are an alternative representation of graphs where each vertex of the original (root) graph becomes an edge. However not all graphs have a corresponding root graph, hence the transformation from graphs to line graphs is not invertible. We investigate the case when…

  • Central limit theorems for the eigenvalues of graph Laplacians on data clouds

    Central limit theorems for the eigenvalues of graph Laplacians on data clouds arXiv:2507.18803v1 Announce Type: new Abstract: Given i.i.d. samples $X_n ={ x_1, dots, x_n }$ from a distribution supported on a low dimensional manifold ${M}$ embedded in Eucliden space, we consider the graph Laplacian operator $Delta_n$ associated to an $varepsilon$-proximity graph over $X_n$ and…

  • Minimax Rates for the Estimation of Eigenpairs of Weighted Laplace-Beltrami Operators on Manifolds

    Minimax Rates for the Estimation of Eigenpairs of Weighted Laplace-Beltrami Operators on Manifolds arXiv:2506.00171v1 Announce Type: new Abstract: We study the problem of estimating eigenpairs of elliptic differential operators from samples of a distribution $rho$ supported on a manifold $M$. The operators discussed in the paper are relevant in unsupervised learning and in particular are…

  • A Kernelised Stein Discrepancy for Assessing the Fit of Inhomogeneous Random Graph Models

    A Kernelised Stein Discrepancy for Assessing the Fit of Inhomogeneous Random Graph Models arXiv:2505.21580v1 Announce Type: new Abstract: Complex data are often represented as a graph, which in turn can often be viewed as a realisation of a random graph, such as of an inhomogeneous random graph model (IRG). For general fast goodness-of-fit tests in…

  • LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks

    LOBSTUR: A Local Bootstrap Framework for Tuning Unsupervised Representations in Graph Neural Networks arXiv:2505.14867v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are increasingly used in conjunction with unsupervised learning techniques to learn powerful node representations, but their deployment is hindered by their high sensitivity to hyperparameter tuning and the absence of established methodologies for…

  • Efficient Graph Storage for Entity Resolution Using Clique-Based Compression

    Efficient Graph Storage for Entity Resolution Using Clique-Based Compression In the world of entity resolution (ER), one of the central challenges is managing and maintaining the complex relationships between records. At its core, Tilores models entities as graphs: each node represents a record, and edges represent rule-based matches between those records. This approach gives us…

  • Government Funding Graph RAG

    Government Funding Graph RAG In this article, I present my latest open-source project — Government Funding Graph. The inspiration for this project came from a desire to make better tooling for grant writing, namely to suggest research topics, funding bodies, research institutions, and researchers. I have made Innovate UK grant applications in the past, so I have…

  • Graph Neural Networks Part 3: How GraphSAGE Handles Changing Graph Structure

    Graph Neural Networks Part 3: How GraphSAGE Handles Changing Graph Structure In the previous parts of this series, we looked at Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs). Both architectures work fine, but they also have some limitations! A big one is that for large graphs, calculating the node representations with GCNs and…

  • Pure Exploration with Feedback Graphs

    Pure Exploration with Feedback Graphs arXiv:2503.07824v1 Announce Type: new Abstract: We study the sample complexity of pure exploration in an online learning problem with a feedback graph. This graph dictates the feedback available to the learner, covering scenarios between full-information, pure bandit feedback, and settings with no feedback on the chosen action. While variants of…

  • Are GNNs doomed by the topology of their input graph?

    Are GNNs doomed by the topology of their input graph? arXiv:2502.17739v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, the influence of the input graph’s topology on GNN behavior remains poorly understood. In this work, we explore whether GNNs are inherently limited by the structure…

  • Graph Signal Inference by Learning Narrowband Spectral Kernels

    Graph Signal Inference by Learning Narrowband Spectral Kernels arXiv:2502.13686v1 Announce Type: new Abstract: While a common assumption in graph signal analysis is the smoothness of the signals or the band-limitedness of their spectrum, in many instances the spectrum of real graph data may be concentrated at multiple regions of the spectrum, possibly including mid-to-high-frequency components.…

  • How to Create Network Graph Visualizations in Microsoft PowerBI

    How to Create Network Graph Visualizations in Microsoft PowerBI Microsoft PowerBI is a one of the most popular Business Intelligence (BI) tools, and while it has all the features you need to create dynamic analytic reporting for stakeholders across the business, creating some advanced data visualizations is more challenging. This article will walk through how…

  • Static and Dynamic Attention: Implications for Graph Neural Networks

    Static and Dynamic Attention: Implications for Graph Neural Networks Examining the expressive capacity of Graph Attention Networks Image by the author In graph representation learning, neighborhood aggregation is one of the most well-studied and investigated areas, among which attention-based methods largely remain state-of-the-art. Leveraging learnable attention scores for weighted aggregations, graph attention networks exhibit higher expressivity…

  • Structure-Preference Enabled Graph Embedding Generation under Differential Privacy

    Structure-Preference Enabled Graph Embedding Generation under Differential Privacy arXiv:2501.03451v1 Announce Type: new Abstract: Graph embedding generation techniques aim to learn low-dimensional vectors for each node in a graph and have recently gained increasing research attention. Publishing low-dimensional node vectors enables various graph analysis tasks, such as structural equivalence and link prediction. Yet, improper publication opens…

  • Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks

    Modeling COVID-19 spread in the USA using metapopulation SIR models coupled with graph convolutional neural networks arXiv:2501.02043v1 Announce Type: new Abstract: Graph convolutional neural networks (GCNs) have shown tremendous promise in addressing data-intensive challenges in recent years. In particular, some attempts have been made to improve predictions of Susceptible-Infected-Recovered (SIR) models by incorporating human mobility…

  • 3D Clustering with Graph Theory: The Complete Guide

    3D Clustering with Graph Theory: The Complete Guide Python Tutorial for Euclidean Clustering of 3D Point Clouds with Graph Theory. Fundamental concepts and sequential workflow for… Continue reading on Towards Data Science » Florent Poux, Ph.D. Go to original source

  • Graph Max Shift: A Hill-Climbing Method for Graph Clustering

    Graph Max Shift: A Hill-Climbing Method for Graph Clustering arXiv:2411.18794v1 Announce Type: new Abstract: We present a method for graph clustering that is analogous with gradient ascent methods previously proposed for clustering points in space. We show that, when applied to a random geometric graph with data iid from some density with Morse regularity, the…