Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Computationally Tractable Riemannian Manifolds for Graph Embeddings
Authors: Calin Cruceru, Gary Becigneul, Octavian-Eugen Ganea7133-7141
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate consistent improvements over Euclidean geometry while often outperforming hyperbolic and elliptical embeddings based on various metrics that capture different graph properties. Our results serve as new evidence for the benefits of non-Euclidean embeddings in machine learning pipelines. |
| Researcher Affiliation | Collaboration | 1 Department of Computer Science, ETH Z urich, Switzerland 2 Computer Science and Artificial Intelligence Lab, MIT, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Facebook (Leskovec and Mcauley 2012) used in our experiments. It shows the Ollivier-Ricci curvatures of edges and their averages for nodes. More such drawings are included in Appendix H. (...) We embed several graphs with traits associated with positive curvature in Grassmann manifolds and compare them to spherical embeddings. Table 3 shows that the former yields non-negligibly lower average distortion on the cat-cortex dissimilarity dataset (Scannell, Blakemore, and Young 1995) |
| Dataset Splits | No | The paper does not specify exact training/validation/test splits, percentages, or absolute sample counts for the datasets used. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | No | The paper mentions optimizing embeddings for |