Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A nonlinear diffusion method for semi-supervised learning on hypergraphs   Hypergraphs are a common model for multiway relationships in data, and
hypergraph semi-supervised 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 predictions. Here, we develop a
nonlinear diffusion process on hypergraphs that spreads both features and
labels following the hypergraph structure, which can be interpreted as a
hypergraph equilibrium network. Even though the process is nonlinear, we show
global convergence to a unique limiting point for a broad class of
nonlinearities, which is the global optimum of a interpretable, regularized
semi-supervised learning loss function. The limiting point serves as a node
embedding from which we make predictions with a linear model. Our approach is
much more accurate than several hypergraph neural networks, and also takes less
time to train.

13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys