# A nonlinear diffusion method for semi-supervised learning on hypergraphs

@article{Tudisco2021AND, title={A nonlinear diffusion method for semi-supervised learning on hypergraphs}, author={Francesco Tudisco and Konstantin Prokopchik and Austin R. Benson}, journal={ArXiv}, year={2021}, volume={abs/2103.14867} }

Hypergraphs are a common model for multiway relationships in data, and hypergraph semisupervised learning is the problem of assigning labels to all nodes in a hypergraph, given labels on just a few nodes. Diffusions and label spreading are classical techniques for semi-supervised learning in the graph setting, and there are some standard ways to extend them to hypergraphs. However, these methods are linear models, and do not offer an obvious way of incorporating node features for making…

## 6 Citations

### Equivariant Hypergraph Diffusion Neural Operators

- Computer ScienceArXiv
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This work proposes a new HNN architecture named ED-HNN, which provably approximates any continuous equivariant hypergraph diffusion operators that can model a wide range of higher-order relations and shows great superiority in processing heterophilic hypergraphs and constructing deep models.

### Information Limits for Community Detection in Hypergraph with Label Information

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This work investigating the effect of label information on the exact recovery of communities in an m-uniform Hypergraph Stochastic Block Model (HSBM) derives sharp boundaries for exact recovery under both scenarios from an information-theoretical point of view.

### Core-periphery detection in hypergraphs

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This work proposes a model of core-periphery for higher-order networks modeled as hypergraphs and proposes a method for computing a core-score vector that quantifies how close each node is to the core.

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The general nature of this embedding strategy opens up many emerging applications, where eigenvector and spectral techniques may not be well established, to the PageRank-based relatives, for instance, similar techniques can be used on PageRank vectors from hypergraphs to get “spectral-like” embeddings.

### You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks

- Computer ScienceICLR
- 2022

AllSet is proposed, a new hypergraph neural network paradigm that represents a highly general framework for (hyper)graph neural networks and for the first time implements hyper graph neural network layers as compositions of two multiset functions that can be efficiently learned for each task and each dataset.

### A Survey on Hyperlink Prediction

- Computer ScienceArXiv
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A new taxonomy is proposed to classify existing hyperlink prediction methods into four categories: similarity- based, probability-based, matrix optimized, and deep learning-based methods.

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