Creating generalizable downstream graph models with random projections
- URL: http://arxiv.org/abs/2302.08895v1
- Date: Fri, 17 Feb 2023 14:27:00 GMT
- Title: Creating generalizable downstream graph models with random projections
- Authors: Anton Amirov, Chris Quirk, Jennifer Neville
- Abstract summary: We investigate graph representation learning approaches that enable models to generalize across graphs.
We show that using random projections to estimate multiple powers of the transition matrix allows us to build a set of isomorphism-invariant features.
The resulting features can be used to recover enough information about the local neighborhood of a node to enable inference with relevance competitive to other approaches.
- Score: 22.690120515637854
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We investigate graph representation learning approaches that enable models to
generalize across graphs: given a model trained using the representations from
one graph, our goal is to apply inference using those same model parameters
when given representations computed over a new graph, unseen during model
training, with minimal degradation in inference accuracy. This is in contrast
to the more common task of doing inference on the unseen nodes of the same
graph. We show that using random projections to estimate multiple powers of the
transition matrix allows us to build a set of isomorphism-invariant features
that can be used by a variety of tasks. The resulting features can be used to
recover enough information about the local neighborhood of a node to enable
inference with relevance competitive to other approaches while maintaining
computational efficiency.
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