Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning
Authors: Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS Datasets. We use six large KG datasets: FB15K, FB15K-237, WN18, WN18RR, NELL995, and YAGO3-10. Experimental settings. We use the same data split protocol as in many papers... Comparison results and analysis. We report comparison on FB15K-23 and WN18RR in Table 2. Our model DPMPN significantly outperforms all the baselines in HITS@1,3 and MRR. |
| Researcher Affiliation | Collaboration | Xiaoran Xu1, Wei Feng1, Yunsheng Jiang1, Xiaohui Xie1, Zhiqing Sun2, Zhi-Hong Deng3 1Hulu, {xiaoran.xu, wei.feng, yunsheng.jiang, xiaohui.xie}@hulu.com 2Carnegie Mellon University, zhiqings@andrew.cmu.edu 3Peking University, zhdeng@pku.edu.cn |
| Pseudocode | No | The paper describes the model architecture and operations mathematically and textually but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is written in Python based on Tensor Flow 2.0 and Num Py 1.16 and can be found by the link3 below. https://github.com/anonymousauthor123/DPMPN |
| Open Datasets | Yes | Datasets. We use six large KG datasets: FB15K, FB15K-237, WN18, WN18RR, NELL995, and YAGO3-10. FB15K-237 (Toutanova & Chen, 2015) is sampled from FB15K (Bordes et al., 2013)... WN18RR (Dettmers et al., 2018) is a subset of WN18 (Bordes et al., 2013)... NELL995 (Xiong et al., 2017)... YAGO3-10 (Mahdisoltani et al., 2014). |
| Dataset Splits | Yes | We use the same data split protocol as in many papers (Dettmers et al., 2018; Xiong et al., 2017; Das et al., 2018). We create a KG, a directed graph, consisting of all train triples and their inverse added for each dataset... Table 1: Statistics of the six KG datasets. #Train #Valid #Test |
| Hardware Specification | Yes | We run our experiments using a 12G-memory GPU, TITAN X (Pascal), with Intel(R) Xeon(R) CPU E5-2670 v3 @ 2.30GHz. |
| Software Dependencies | Yes | Our code is written in Python based on Tensor Flow 2.0 and Num Py 1.16 and can be found by the link3 below. |
| Experiment Setup | Yes | See hyperparameter details in the appendix. Appendix 8 HYPERPARAMETER SETTINGS Table 3: Our standard hyperparameter settings we use for each dataset plus their one-epoch training time. For experimental analysis, we only adjust one hyperparameter and keep the remaining fixed as the standard setting. For NELL995, the one-epoch training time means the average time cost of the 12 single-query-relation tasks. |