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..
PolygonE: Modeling N-ary Relational Data as Gyro-Polygons in Hyperbolic Space
Authors: Shiyao Yan, Zequn Zhang, Xian Sun, Guangluan Xu, Shuchao Li, Qing Liu, Nayu Liu, Shensi Wang4308-4317
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that Polygon E shows SOTA performance on all benchmark datasets and generalizes well on binary data. |
| Researcher Affiliation | Academia | 1 Aerospace Information Research Institute, Chinese Academy of Sciences 2 Key Laboratory of Network Information System Technology(NIST), Aerospace Information Research Institute 3 School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes the model mathematically but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Knowledge base completion (KBC) experiments are conducted on JF17K (Wen et al. 2016), Wiki People (Guan et al. 2019), and FB-AUTO (Fatemi et al. 2020). |
| Dataset Splits | Yes | Table 1: Statistics of Datasets (showing #Train, #Valid, #Test columns with specific numbers for each dataset like Wiki People: #Train 305,725, #Valid 38,223, #Test 38,281). |
| Hardware Specification | Yes | Experiments are implemented on a single NVIDIA RTX 3080 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming language versions or library versions (e.g., PyTorch 1.x). |
| Experiment Setup | Yes | Embedding dimensions are set to 50 for a fair comparison with RAM. Other hyper-parameters are chosen from grid search. Concretely, learning rate η is selected from {10, 15, 30, 50, 100}, batch size nbatch are chosen from {64, 128, 256}, number of negative samples nneg are selected from {25, 50, 100}. α and β in equation (13) are integers sampled from {1, 2, 3, 4, 5, 6}. Experiments are implemented on a single NVIDIA RTX 3080 GPU. |