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..
Robust Embedding with Multi-Level Structures for Link Prediction
Authors: Zihan Wang, Zhaochun Ren, Chunyu He, Peng Zhang, Yue Hu
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Results on WN18 and FB15k datasets show that our approach is effective in the standard link prediction task, significantly and consistently outperforming competitive baselines. Furthermore, robustness analysis on FB15k-237 dataset demonstrates that our proposed M-GNN is highly robust to sparsity and noise. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 2School of Cyber Security, University of Chinese Academy of Sciences 3Shandong University |
| Pseudocode | Yes | Algorithm 1 Graph Coarsening. Require: Knowledge graph KG = (V, E, R); Ensure: Coarsened graph G0, G1, ..., Gk; 1: m 0 2: G0 KG 3: while |Em| threshold do 4: m m + 1; 5: Gm Edge Coarsen(Neighbor Coarsen(Gm 1)) 6: end while 7: return G0, G1, ..., Gk; |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate our link prediction algorithm on two commonly used datasets: FB15k, a subset of the multi-label knowledge base Freebase and WN18, a subset of Word Net featuring lexical relations between words. Both datasets are released by [Bordes et al., 2013]. ... [Toutanova and Chen, 2015] release the dataset FB15k-237 removing all inverse triplet pairs. Thus, We use FB15k-237 dataset for our extensive experiments. |
| Dataset Splits | Yes | Dataset # Ent # Rel # Train /Valid/Test WN18 40943 18 141442/5000/5000 FB15k 14951 1345 483142/50000/59071 FB15k-237 14541 237 272115/17535/20466 ... The hyperparameters in M-GNN are determined by the grid search on the validation set. |
| Hardware Specification | No | The paper mentions training models but does not specify any hardware details such as CPU/GPU models, memory, or specific cloud computing resources used for the experiments. |
| Software Dependencies | No | The paper mentions 'We train the models with Adam optimizer [Kingma and Ba, 2015]' but does not provide specific version numbers for any software libraries, frameworks, or environments used (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | The hyperparameters in M-GNN are determined by the grid search on the validation set. The ranges of the hyperparameters are manually set as follow: learning rate {0.01, 0.005, 0.003, 0.001}, dropout rate {0, 0.1, 0.2, 0.3,...,0.9}, embeddings size {100, 150, 200, 300}, regularization coefficient {0.01,0.05,0.1,0.5,1.0}, the number of negative samples {1,3,5,10} and ϵ = 0. For both FB15k and WN18 datasets, we use M-GNN with three GNN layers and all MLPs have two layers with the hidden unit number {10,50,100,200}. For the Compl Ex encoder, we treat complex vector Cd as real vector Rd 2 in the encoder. We train the models with Adam optimizer [Kingma and Ba, 2015]. |