arXiv Machine Learning
Jun 2
Efficient Synthetic Network Generation via Latent Embedding Reconstruction
arXiv:2606. 00934v1 Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems.
Weighted Random Dot Product Graphs
arXiv:2505. 03649v4 Announce Type: replace-cross Abstract: Modeling of intricate relational patterns has become a cornerstone of contemporary statistical research and related data science fields.
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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.
arXiv Machine Learning
Aug 12
Spectral Embeddings of Degree-$\alpha$ Laplacians in Random Dot Product Graphs
arXiv:2608. 10845v1 Announce Type: cross Abstract: Spectral clustering methods for network data are commonly based on a few matrix representations, such as the adjacency matrix and the symmetric Laplacian.
arXiv Machine Learning
5d ago
Embedding networks with the random walk first return time distribution
arXiv:2512. 02694v3 Announce Type: replace-cross Abstract: We propose the first return time distribution (FRTD) of a random walk as an interpretable and mathematically grounded node embedding.
arXiv Machine Learning
Jul 14
Distance-Preserving Embeddings in Inhomogeneous Random Graphs
arXiv:2607. 10074v1 Announce Type: new Abstract: Graph machine learning provides powerful tools for understanding complex networks and learning meaningful node representations.
arXiv AI
Jul 9
DiPhon: Diffusion on Graphons for Scalable Graph Generation
arXiv:2607. 07232v1 Announce Type: cross Abstract: Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design.
Hugging Face Trending Papers
Jul 8
DiPhon: Diffusion on Graphons for Scalable Graph Generation
Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an open problem.
arXiv Machine Learning
Jun 18
Robust Detection of Planted Subgraphs in Semi-Random Models
arXiv:2508. 02158v2 Announce Type: replace-cross Abstract: Detection of planted subgraphs in Erd\"os-R\'enyi random graphs has been extensively studied, leading to a rich body of results characterizing both statistical and computational thresholds.
arXiv Machine Learning
Jun 10
When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice
arXiv:2606. 10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs.
arXiv Machine Learning
Jul 30
HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings
arXiv:2605. 16836v2 Announce Type: replace-cross Abstract: Hypergraphs provide a principled framework for modeling polyadic interactions, with applications in recommendation systems, social networks, and molecular modeling.
arXiv Machine Learning
Jun 19
Prior-Informed Flow Matching for Graph Reconstruction
arXiv:2601. 22107v2 Announce Type: replace Abstract: We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction.
arXiv Machine Learning
Jun 5
The ASE-LSE Disagreement Landscape: An End-to-End Characterisation of Extremes and Structural Drivers
arXiv:2605. 22346v2 Announce Type: replace-cross Abstract: Two of the most widely used methods for analysing graph data, Adjacency Spectral Embedding and Laplacian Spectral Embedding, often produce different results when applied to the same graph.