Related papers: HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network

HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network

URL: http://arxiv.org/abs/2402.09676v1
Date: Thu, 15 Feb 2024 03:05:45 GMT
Title: HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network
Authors: Tatyana Benko, Martin Buck, Ilya Amburg, Stephen J. Young, Sinan G. Aksoy
Abstract summary: We propose an alternative approach to hypergraph neural networks in which the hypergraph is represented as a non-reversible Markov chain. We use this Markov chain to construct a complex Hermitian Laplacian matrix - the magnetic Laplacian - which serves as the input to our proposed hypergraph neural network.
Score: 0.16874375111244327
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In data science, hypergraphs are natural models for data exhibiting multi-way relations, whereas graphs only capture pairwise. Nonetheless, many proposed hypergraph neural networks effectively reduce hypergraphs to undirected graphs via symmetrized matrix representations, potentially losing important information. We propose an alternative approach to hypergraph neural networks in which the hypergraph is represented as a non-reversible Markov chain. We use this Markov chain to construct a complex Hermitian Laplacian matrix - the magnetic Laplacian - which serves as the input to our proposed hypergraph neural network. We study HyperMagNet for the task of node classification, and demonstrate its effectiveness over graph-reduction based hypergraph neural networks.

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