Table of Contents
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Graph neural networks transform the nodes of a graph into a high dimensional latent space.
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This project will contrast the distances between nodes of a graph in the input space (graph structure) to their embedding in the latent space.
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Queries of interest will be finding node/subgraph outliers, and comparing representations produced by different deep learning methods.
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Project can be extended to consider different ways of reducing distortions in embeddings and measuring the local dimensionality of the embedding space.
As we are investigating two different database softwares to build on top of, we have two different forks of those repositories.
Deprecated:
- ArangoDB fork: arangodb_ersp
- OpenCypher fork: openCypher_ersp
Active:
- Minimum Edge Set Perturbation: minimum-edge-set-perturbation
- Homophily: homophily
- Degree: degree
- Common Neighbors: common-neighbors
Distributed under the MIT License. See LICENSE
for more information.
Will Corcoran - wcorcoran@ucsb.edu
Wyatt Hamabe - whamabe@ucsb.edu
Niyati Mummidivarapu - niyati@ucsb.edu
Danish Ebadulla - danish_ebadulla (at) umail (dot) ucsb (dot) edu