M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
Authors: Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several graph-walking benchmarks show that M-Walk is able to learn better policies than other RL-based methods, which are mainly based on policy gradients. M-Walk also outperforms traditional KBC baselines. |
| Researcher Affiliation | Industry | 1Tencent AI Lab, Bellevue, WA, USA. {yelongshen, jianshuchen}@tencent.com 2Microsoft Research, Redmond, WA, USA {yuqguo, jfgao}@microsoft.com |
| Pseudocode | No | The paper describes the training and prediction algorithms in text but does not include any formally labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | The code of this paper is available at: https://github.com/yelongshen/Graph Walk |
| Open Datasets | Yes | We use WN18RR and NELL995 knowledge graph datasets for evaluation. WN18RR [6] is created from the original WN18 [2]... The NELL995 dataset was released by [38]... |
| Dataset Splits | Yes | We use the same data split and preprocessing protocol as in [6] for WN18RR and in [38, 5] for NELL995. |
| Hardware Specification | No | The paper mentions 'Cuda' and 'Tensor Flowgpu' which implies GPU usage for computations, but it does not specify any particular GPU models, CPU models, or other detailed hardware specifications for the experimental setup. |
| Software Dependencies | No | The paper mentions software like 'Tensor Flowgpu' and 'Cuda' and implementation languages like 'C++', but it does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | The paper states: 'We briefly describe the tasks here, and give the experiment details and hyperparameters in Appendix B.' and 'The details of the hyperparameters for M-Walk are described in Appendix B.2.2 of the supplementary material.' This indicates that the specific experimental setup details, including hyperparameters, are deferred to the supplementary material and are not present in the main text. |