@article{ebli_simplicial_2020,
abstract = {We present simplicial neural networks ({SNNs}), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes. These are natural multi-dimensional extensions of graphs that encode not only pairwise relationships but also higher-order interactions between vertices - allowing us to consider richer data, including vector fields and \$n\$-fold collaboration networks. We define an appropriate notion of convolution that we leverage to construct the desired convolutional neural networks. We test the {SNNs} on the task of imputing missing data on coauthorship complexes.},
author = {Ebli, Stefania and Defferrard, MichaĆ«l and Spreemann, Gard},
date = {2020-12-28},
eprint = {2010.03633},
eprinttype = {arxiv},
journaltitle = {{arXiv}:2010.03633 [cs, math, stat]},
keywords = {1 - Coauthorship, 1 - Machine learning, 1 - Missing data, 1 - Neural network, 2 - Network, 2 - Simplicial neural network, 3 - Graphs, 3 - Neural Networks},
title = {Simplicial Neural Networks},
url = {http://arxiv.org/abs/2010.03633},
urldate = {2021-01-06}
}