Multi-View Joint Graph Representation Learning for Urban Region Embedding
Authors: Mingyang Zhang, Tong Li, Yong Li, Pan Hui
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results demonstrate that by exploiting our proposed joint learning model, the performance is improved by a large margin on both tasks compared with the state-of-the-art methods. |
| Researcher Affiliation | Academia | Mingyang Zhang1 , Tong Li1,3 , Yong Li2 and Pan Hui1,3 1The Hong Kong University of Science and Technology 2Tsinghua University 3University of Helsinki |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Please refer to the released code2 for details of the implementations. 2https://github.com/mingyangzhang/mv-region-embedding |
| Open Datasets | Yes | We collect several real-world datasets of New York City from NYC open data website1. The description of each dataset is shown in Table 1. 1opendata.cityofnewyork.us |
| Dataset Splits | Yes | compute all the metrics by K-Fold cross-validation, where K=5. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running experiments. |
| Software Dependencies | No | The paper refers to various models and algorithms (e.g., GAT, GAE, node2vec, K-means, Lasso regression) but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions or library versions). |
| Experiment Setup | Yes | The embedding sizes of CHK and Po I are the number of check-in and Po I categories, respectively. The embedding size of HDGE and ZE-Mob are set as 20 and 96 as suggested by the authors. The embedding sizes of GAE, node2vec, MV-PN, our model and variants are set as 96. |