Is is possible to train on Hypergraphs in Keras? I know that vanilla Keras doesn't support operations with graphs.
For example, the Spektral library, based on Keras API, provides some layers to work with simple graphs. However, it doesn't support graphs with multi-node connections (i.e. hypergraphs).
Is there any way to train a deep learning model on the hypergraph input data?
As far as I'm aware, deep learning on hypergraphs is still a relatively new area, so I don't think there's any ready-made solution for hypergraphs. I did find this repo, which implements some models in keras to accompany a recent paper on hypergraph learning, but it is hardly a library.
You may also check out this paper, which cites a pair of techniques for converting a hypergraph to a graph:

Notably, previous methods [for hypergraph learning] typically decompose the hyperedge into pair-wise relationships where the decomposition methods can be divided into two categories: explicit and implicit. For instance, given a hyperedge (v_1,v_2,v_3), the explicit approach would decompose it directly into three edges, $(v_1,v_2), (v_2,v_3), (v_1,v_3)$, while the implicit approach would add a hidden node $e$ representing the hyperedge before decomposition, i.e., $(v_1,e),(v_2,e),(v_3,e)$.

If you're okay with doing this, then there are a few options besides Spektral:
Deep Graph Library provides a Tensorflow.keras compatible API, although their documentation seems to favor PyTorch.
There is also Graph Nets, which is built on top of Tensorflow.keras, although it's quite bare-bones at the moment.