Source code and data for the PhD Thesis "Learning Neural Graph Representations in Non-Euclidean Geometries"

DOI

This dataset contains source code and data used in the PhD thesis "Learning Neural Graph Representations in Non-Euclidean Geometries". The dataset is split into four repositories:

figet: Source code to run experiments for chapter 6 "Constructing and Exploiting Hierarchical Graphs". hyfi: Source code to run experiments for chapter 7 "Inferring the Hierarchy with a Fully Hyperbolic Model". sympa: Source code to run experiments for chapter 8 "A Framework for Graph Embeddings on Symmetric Spaces". gyroSPD: Source code to run experiments for chapter 9 "Representing Multi-Relational Graphs on SPD Manifolds".

Identifier
DOI https://doi.org/10.11588/data/KOAMK4
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/data/KOAMK4
Provenance
Creator Lopez, Federico ORCID logo
Publisher heiDATA
Contributor Lopez, Federico
Publication Year 2023
Rights info:eu-repo/semantics/openAccess
OpenAccess true
Contact Lopez, Federico (Heidelberg University)
Representation
Resource Type Dataset
Format application/zip
Size 2971518; 4900874; 259028; 4382878
Version 1.0
Discipline Other