Multi-Graph Fusion Networks for Urban Region Embedding
Authors: Shangbin Wu, Xu Yan, Xiaoliang Fan, Shirui Pan, Shichao Zhu, Chuanpan Zheng, Ming Cheng, Cheng Wang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement. |
| Researcher Affiliation | Academia | 1Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Department of Computer Science and Technology, Xiamen University, China 2Department of Data Science and AI, Faculty of Information Technology, Monash University, Australia 3Institute of Information Engineering, Chinese Academy of Sciences, China 4School of Cyber Security, University of Chinese Academy of Sciences, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | https://github.com/wushangbin/MGFN |
| Open Datasets | Yes | Data Description We evaluate the performance of our method on New York City (NYC) datasets from NYC open data website 1. 1opendata.cityofnewyork.us |
| Dataset Splits | No | The paper mentions using the NYC dataset but does not provide specific training/validation/test split percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | Parameter Settings Following [Zhang et al., 2020], the dimension of region embeddings d is 96. In mobility graph fusion module, the weight ci in MGD is set as 1, and the number of mobility patterns N is set as 7. In mobility pattern joint learning module, the number of layers L is set as 1. |