Introduction to Graph Machine Learning

Graph Classification with Transformers

Introduction to Graph Neural Networks for Machine Learning Engineers

arXiv:2412. 19419v2 Announce Type: replace-cross Abstract: Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges.

From topology learning to graph generation: A unifying perspective

The article reviews the problem of learning graph structures from data, noting that research has traditionally split into two paths: inferring the topology of a single graph from observations on it, and learning a generative distribution from multiple observed graphs to sample new ones. It proposes a unified framework that treats both as inverse problems of a common graph generation process, reviews key methods, and discusses their interrelations, strengths, and limitations. The review highlights opportunities for cross‑paradigm integration and outlines future research directions.

Graph Neural Networks for the Graphical Bootstrap

arXiv:2607. 03109v1 Announce Type: cross Abstract: We study a graph classification problem involving over 20 million graphs, arising from high-order perturbative computations of correlators in planar $\mathcal{N}=4$ super-Yang--Mills, a model closely related to the theory of the strong nuclear force.

Provable Guarantees for Spectral Structured Prediction

The paper presents provable guarantees for a spectral method that recovers binary node labels on signed graphs with edge‑flip noise. It provides graph‑structure‑agnostic bounds on approximate inference accuracy and maximum angle deviation, using matrix concentration and eigenvector perturbation techniques. The results connect to the Cheeger constant and are validated with synthetic experiments, marking the first theoretical analysis of this spectral approach.

A Survey of Graph Transformers: Architectures, Theories and Applications

arXiv:2502. 16533v3 Announce Type: replace-cross Abstract: Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-smoothing and over-squashing.

Enhanced Graph Neural Networks using K-Hop Gaussian Diffusion

Most graph neural network (GNN) cores rely on graph convolutions, typically implemented as message passing between direct (single-hop) neighbors. In many real-world graphs, edges can be noisy or poorly defined, limiting information propagation to local neighborhoods.

Enhanced Graph Neural Networks using K-Hop Gaussian Diffusion

arXiv:2606. 18317v1 Announce Type: new Abstract: Most graph neural network (GNN) cores rely on graph convolutions, typically implemented as message passing between direct (single-hop) neighbors.

Exphormer: Scaling transformers for graph-structured data

Posted by Ameya Velingker, Research Scientist, Google Research, and Balaji Venkatachalam, Software Engineer, Google Graphs , in which objects and their relations are represented as nodes (or vertices) and edges (or links) between pairs of nodes, are ubiquitous in computing and machine learning (ML). For example, social networks, road networks, and molecular structure and interactions are all domains in which underlying datasets have a natural graph structure.

Fast Graph Laplacian Estimation using Effective Resistance

arXiv:2609.23668v1 Announce Type: cross Abstract: Inferring network topology from noisy node observations is a central problem in graph signal processing. In this paper, we consider Laplacian-constra...

Graph Homomorphism Distortion: A structural (pseudo)-metric for attributed graphs

The paper introduces a new (pseudo)-metric called graph homomorphism distortion, which quantifies how much node features must be distorted when mapping one attributed graph to another. This metric builds on metric geometry concepts and leverages graph homomorphisms to capture both structure and features. The authors show that the metric can be efficiently approximated, offers fine-grained graph distinction, and can serve as a structural encoding that enhances graph neural network predictions.

A dictionary learning framework for graphs via filters and optimal transport

arXiv:2609.05919v1 Announce Type: new Abstract: We propose a graph dictionary learning (GDL) framework where each graph is represented as a zero-mean Gaussian distribution derived from its filtered L...