HyperMagNet: A Magnetic Laplacian based Hypergraph Neural Network
Tatyana Benko Martin Buck Ilya Amburg Stephen J. Young Sinan G. Aksoy
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.