Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets
conference paper
This paper aims for set-to-hypergraph prediction, where the goal is to infer the set
of relations for a given set of entities. This is a common abstraction for applications
in particle physics, biological systems, and combinatorial optimization. We address
two common scaling problems encountered in set-to-hypergraph tasks that limit
the size of the input set: the exponentially growing number of hyperedges and the
run-time complexity, both leading to higher memory requirements. We make three
contributions. First, we propose to predict and supervise the positive edges only,
which changes the asymptotic memory scaling from exponential to linear. Second,
we introduce a training method that encourages iterative refinement of the predicted
hypergraph, which allows us to skip iterations in the backward pass for improved
efficiency and constant memory usage. Third, we combine both contributions in
a single set-to-hypergraph model that enables us to address problems with larger
input set sizes. We provide ablations for our main technical contributions and show
that our model outperforms prior state-of-the-art, especially for larger sets
of relations for a given set of entities. This is a common abstraction for applications
in particle physics, biological systems, and combinatorial optimization. We address
two common scaling problems encountered in set-to-hypergraph tasks that limit
the size of the input set: the exponentially growing number of hyperedges and the
run-time complexity, both leading to higher memory requirements. We make three
contributions. First, we propose to predict and supervise the positive edges only,
which changes the asymptotic memory scaling from exponential to linear. Second,
we introduce a training method that encourages iterative refinement of the predicted
hypergraph, which allows us to skip iterations in the backward pass for improved
efficiency and constant memory usage. Third, we combine both contributions in
a single set-to-hypergraph model that enables us to address problems with larger
input set sizes. We provide ablations for our main technical contributions and show
that our model outperforms prior state-of-the-art, especially for larger sets
TNO Identifier
997130
Source title
Proceedings of the First Learning on Graphs Conference (LoG 2022)
Files
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