arXiv Machine Learning

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.

Hugging Face Trending Papers

Sep 8

Topology-induced Operators Reveal Complementary Graph Representations without Training

The paper demonstrates that high‑quality graph embeddings can be produced without complex models or training by propagating random features through topological structures derived from random walks and anonymous walks. These training‑free embeddings capture node proximity and structural roles, respectively, and perform competitively on node, edge, and graph tasks while often requiring less computation. Combining the two embedding types further improves inference quality for some tasks.

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arXiv Machine Learning

Sep 10

Topology-induced Operators Reveal Complementary Graph Representations without Training

arXiv:2609.08152v1 Announce Type: new Abstract: Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph topology into vector representation...

By Meng Qin, Jinqiang Cui, Hongwei Zheng, Weihua Li, Sen Pei

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arXiv Machine Learning

Oct 5

Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation

arXiv:2610.02517v1 Announce Type: new Abstract: Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection an...

By Yu Song, Zhigang Hua, Yan Xie, Bingheng Li, Jingzhe Liu, Bo Long, Jiliang Tang, Hui Liu

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arXiv Machine Learning

Aug 20

GraphK: Variable-Size Graph Generation with Efficient Edge Construction

GraphK introduces an encoder‑sampler‑decoder framework that generates variable‑size graphs efficiently. It learns permutation‑invariant latent representations and samples new node embeddings via maximum likelihood, enabling both upscaling and downscaling of graph size. Edge construction uses KDTree‑based top‑k neighbor search in latent space, reducing computational cost while capturing graph properties.

By Resul Tugay, Eren Olu\u{g}, Elif Ak, Sule Gunduz Oguducu

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arXiv AI

Sep 25

Beyond Static Graph World Models: Learning Stochastic Latent Dynamics over Evolving Topologies

The paper introduces the Graph Dynamics Model (GDM), a world model that learns stochastic latent dynamics over evolving graph topologies. GDM employs a sparse recurrent adjacency matrix for topology updates and a recurrent state‑space architecture for stochastic transitions, enabling it to handle partially observable, stochastic environments. The authors also propose the Graph Distribution Distance (GDD) metric, using maximum mean discrepancy with a graph kernel, to compare predicted and true joint graph state distributions, and demonstrate GDM’s superior performance and zero‑shot generalisation on large graphs.

By Alex Schutz, Nick Hawes, Victor-Alexandru Darvariu

arXiv AI

Jul 28

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.

By Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, Liang Wang, Tingyang Xu, Wenbing Huang, Deli Zhao, Hong Cheng, Yu Rong

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arXiv AI

Jul 23

A Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models

arXiv:2601. 21207v4 Announce Type: replace-cross Abstract: Combinatorial and topological structures, such as graphs, simplicial complexes, and cell complexes, form the foundation of geometric and topological deep learning (GDL and TDL) architectures.

By Chuan-Shen Hu

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arXiv Machine Learning

Aug 18

Feature-Aware (Hyper)graph Generation via Next-Scale Prediction

arXiv:2506. 01467v4 Announce Type: replace Abstract: Graph generative models perform well on small-scale structured data but struggle to scale to large, complex structures.

By Dorian Gailhard, Enzo Tartaglione, Lirida Naviner, Jhony H. Giraldo

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arXiv Machine Learning

Jun 2

From Moments to Models: Graphon-Mixture Learning for Mixup and Contrastive Learning

arXiv:2510. 03690v4 Announce Type: replace Abstract: Real-world graph datasets often arise from mixtures of populations, where graphs are generated by multiple distinct underlying distributions.

By Ali Azizpour, Reza Ramezanpour, Santiago Segarra

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arXiv Machine Learning

Aug 10

Hyperbolic Graph Embedders for Link Prediction and Topology Reconstruction

arXiv:2608. 07029v1 Announce Type: new Abstract: Hyperbolic embeddings provide compact geometric representations of complex networks in hyperbolic spaces, but systematic comparisons of methods developed in machine learning, network science, and algorithmics remain rare.

By Robert Jankowski, Maksim Kitsak, Dorota Celi\'nska-Kopczy\'nska

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arXiv Machine Learning

Sep 10

Structural Entropy-Driven Graph Diffusion Generation for One-Shot Federated Graph Learning

arXiv:2609.06499v1 Announce Type: new Abstract: One-shot federated graph learning (FGL) requires the server to estimate client contributions from highly compressed information, yet conventional volum...

By Shutong Zheng, Lele Fu, Sheng Huang, Wei Yang Bryan Lim, Chuan Chen

diffusion safety

arXiv AI

Jun 2

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.

By James H. Tanis, Chris Giannella, Adrian V. Mariano, Daoud Meerzaman

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