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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Capacity and Bias of Learned Geometric Embeddings for Directed Graphs
Authors: Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L Clarkson, Andrew McCallum
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform rigorous empirical evaluations of vector, hyperbolic, and region-based geometric representations on several families of synthetic and realworld directed graphs. |
| Researcher Affiliation | Collaboration | 1 University of Massachusetts Amherst 2 IBM Research |
| Pseudocode | No | The paper describes methods and models using mathematical equations and textual explanations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and data are available at https://github.com/iesl/geometric_graph_embedding. |
| Open Datasets | Yes | Graphs Real World. We also select the following real world graph datasets: Word Net (Animals) [34]... Hierarchical Clustering. We run agglomerative clustering on the Inception V3 [56] features from 213 Image Net images [47]. |
| Dataset Splits | No | We check the training loss ten times per epoch, and apply early-stopping with a patience of just over 2 epochs (21 loss observations). The paper mentions early stopping which implies a validation set, but it does not specify explicit training/validation/test splits (e.g., percentages or sample counts) for the datasets used. |
| Hardware Specification | No | The paper mentions 'high performance computing equipment' in the acknowledgements but does not provide specific hardware details such as exact GPU/CPU models or memory amounts used for experiments. |
| Software Dependencies | No | The paper mentions using W&B [5] for hyperparameter optimization but does not provide specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | All models are tuned on learning rate, batch size, and weight of negative loss. We tune the margin γ for OE, and β parameters in (9) for HYPERBOLIC, intersection and volume temperature for BOX, and the initialization of these temperatures for T-BOX. We check the training loss ten times per epoch, and apply early-stopping with a patience of just over 2 epochs (21 loss observations). |