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arXiv Machine Learning

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.

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arXiv Machine Learning
Jul 20

From Diffusion to Reaction-Diffusion: A Dynamical-Systems View of Oversmoothing in Hypergraph Neural Networks

arXiv:2607. 15773v1 Announce Type: new Abstract: Higher-order couplings enhance the expressive power of hypergraph neural networks (HGNNs), but they also intensify representation collapse in deep propagation due to strong multi-way feature mixing.

By Zhiheng Zhou, Mengyao Zhou, Yancheng Chen, Dengyi Zhao, Xingqin Qi, Guiying Yan
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arXiv Machine Learning
Aug 28

Aitchison Embeddings for Learning Compositional Graph Representations

The paper introduces a compositional graph embedding framework based on Aitchison geometry, where nodes are represented as simplex-valued mixtures over latent archetypal factors. By embedding these mixtures using isometric log-ratio coordinates, the method preserves Aitchison distances while allowing unconstrained optimization in Euclidean space, yielding intrinsically interpretable embeddings. The approach achieves competitive performance on node classification and link prediction tasks and enables principled component restriction through subcompositional coherence, allowing analysis of how archetype groups influence representations and predictions.

By Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas, Michail Chatzianastasis, Giannis Nikolentzos
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arXiv AI
Sep 7

Hyperedge Anomaly Detection with Hypergraph Neural Network

The paper introduces an unsupervised hypergraph neural network designed to detect anomalous hyperedges—higher-order associations that deviate from typical patterns. Unlike conventional graph methods that capture only pairwise relationships, this approach leverages hypergraphs to model associations among any number of entities. Experiments on real-life datasets show the model effectively identifies unusual hyperedges without requiring labeled data.

By Md. Tanvir Alam, Md. Mahmudur Rahman, Md. Fahim Arefin, Chowdhury Farhan Ahmed, Zisan Mahmud, Md. Sadman Sakib, Carson K. Leung
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arXiv Machine Learning
Sep 24

Graph Learning with Spectral Connectivity Priors for Scarce Data

The paper introduces Spectral Connectivity-Regularized Graph Learning (SCoGL), a method for learning sparse graphs from limited data by incorporating Laplacian spectral priors that promote global connectivity. SCoGL extends the graphical lasso objective with a connectivity prior derived from Laplacian eigenvalues and uses projected gradient descent with Armijo backtracking for optimization. Experiments demonstrate that SCoGL improves graph recovery and enhances downstream tasks such as graph signal denoising when observations are scarce.

By Mingxiao Liu (Tsinghua University, China), Bahar Oveisgharan (York University, Canada), Bingyan Zou (Tsinghua University, China), Gene Cheung (York University, Canada), H. Vicky Zhao (Tsinghua University, China), Feifei Gao (Tsinghua University, China)
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