HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network

arXiv:2402.09676v1 Announce Type: new
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 …

abstract arxiv cs.lg data data science graphs hypergraph information matrix natural network networks neural network neural networks reduce relations science type via