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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Authors: Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 27,558 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,698 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that INGRAM outperforms 14 different state-of-the-art methods on varied inductive learning scenarios. |
| Researcher Affiliation | Academia | 1School of Computing, KAIST, Daejeon, South Korea. Correspondence to: Joyce Jiyoung Whang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Embeddings via INGRAM at Inference Time |
| Open Source Code | Yes | 1https://github.com/bdi-lab/In Gram |
| Open Datasets | Yes | We create 12 datasets using three benchmarks, NELL995 (Xiong et al., 2017),Wikidata68K (Gesese et al., 2022), and FB15K237 (Toutanova & Chen, 2015). |
| Dataset Splits | Yes | Einf is divided into three pairwise disjoint sets, such that Einf := Finf Tval Ttest with a ratio of 3:1:1. ... We divide Etr into Ftr and Ttr with a ratio of 3:1. |
| Hardware Specification | Yes | All experiments were conducted with Ge Force RTX 2080 Ti, Ge Force RTX 3090 or RTX A6000, depending on the implementations of each method. |
| Software Dependencies | No | The paper mentions using 'the official C++ implementation of node2vec' but does not provide specific version numbers for this or any other software component used in the experiments. |
| Experiment Setup | Yes | We set d = 32 and bd = 32 for INGRAM and all the baseline methods. ... We tuned INGRAM with 10 negative samples, d {32, 64, 128, 256}, bd {128, 256}, L {1, 2, 3}, b L {2, 3, 4}, K {8, 16}, b K {8, 16}, γ {1.0, 1.5, 2.0, 2.5}, B {1, 5, 10} and the learning rate {0.0005, 0.001}. |