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..
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones
Authors: Yushi Bai, Zhitao Ying, Hongyu Ren, Jure Leskovec
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on standard knowledge graph benchmarks show that Con E obtains state-of-the-art performance on hierarchical reasoning tasks as well as knowledge graph completion task on hierarchical graphs. (Abstract) and Given a KG containing many hierarchical and non-hierarchical relations, our experiments evaluate: (A) Performance of Con E on hierarchical reasoning task of predicting if entity h1 is an ancestor of entity h2. (B) Performance of Con E on generic KG completion tasks. (Section 4) |
| Researcher Affiliation | Academia | Yushi Bai EMAIL Tsinghua University; Rex Ying EMAIL Stanford University; Hongyu Ren EMAIL Stanford University; Jure Leskovec EMAIL Stanford University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of our paper is available at http://snap.stanford.edu/cone. (Section 4) |
| Open Datasets | Yes | We use four knowledge graph benchmarks (Table 1): Word Net lexical knowledge graph (WN18RR [5, 17]), drug knowledge graph (DDB14 [18]), and a KG capturing common knowledge (FB15k-237 [31]). Furthermore, we also curated a new biomedical knowledge graph GO21, which models genes and the hierarchy of biological processes they participate in. (Section 4) and Table 1 includes a 'training' column for dataset statistics. |
| Dataset Splits | Yes | Table 1: Datasets statistics. #training #validation #test (Table 1 clearly provides a 'validation' column with sample counts for each dataset). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Adam [32] as the optimizer' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | During training, we use Adam [32] as the optimizer and search hyperparameters including batch size, embedding dimension, learning rate, angle loss weight and dimension of subspace for each hierarchical relation. (Section 4) |