Robust Embedding with Multi-Level Structures for Link Prediction
Authors: Zihan Wang, Zhaochun Ren, Chunyu He, Peng Zhang, Yue Hu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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]. |