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.
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.
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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 Machine Learning
Aug 4
Differentiable Lifting for Topological Neural Networks
arXiv:2608. 01160v1 Announce Type: new Abstract: Topological neural networks (TNNs) enable leveraging high-order structures on graphs (e.
arXiv AI
Jun 4
Scaling Novel Graph Generation via Lightweight Structure-Guided Autoregressive Models
arXiv:2606. 04287v1 Announce Type: cross Abstract: Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity, and beyond.
arXiv Machine Learning
Jun 25
A Hybrid TGN-SEAL Model for Dynamic Graph Link Prediction
arXiv:2602. 14239v3 Announce Type: replace-cross Abstract: Predicting links in sparse, continuously evolving networks is a central challenge in network science.
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
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HyPE-GT: where Graph Transformers meet Hyperbolic Positional Encodings
arXiv:2312. 06576v2 Announce Type: replace Abstract: Graph Transformers (GTs) facilitate the comprehension of complex relationships on graph-structured data by leveraging self-attention of the possible pairs of nodes.
arXiv AI
Aug 3
On the Expressive Power of Sparse Geometric MPNNs
arXiv:2407. 02025v5 Announce Type: replace-cross Abstract: Motivated by applications in chemistry and other sciences, we study the expressive power of message-passing neural networks for geometric graphs, whose node features correspond to 3-dimensional positions.
arXiv Machine Learning
Jun 5
Implicit Bias and Invariance: How Hopfield Networks Efficiently Learn Graph Orbits
arXiv:2512. 14338v3 Announce Type: replace Abstract: Many learning problems involve symmetries, and while invariance can be built into neural architectures, it can also emerge implicitly when training on group-structured data.
arXiv Machine Learning
Jul 10
Structure Learning on Clustered Data
arXiv:2607. 08238v1 Announce Type: new Abstract: Recent algorithmic advances have made directed acyclic graph (DAG) structure learning scalable for causal discovery.
arXiv Machine Learning
Aug 7
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
arXiv:2608. 06031v1 Announce Type: new Abstract: Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts.
arXiv Machine Learning
Jun 25
Ramanujan Graph Rewiring with Non Negative Resistance Curvature
arXiv:2606. 21333v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning on graph-structured data by iteratively propagating and aggregating information across edges.