Multiplex Graph Representation Learning with Homophily and Consistency
Authors: Yudi Huang, Ci Nie, Hongqing He, Yujie Mo, Yonghua Zhu, Guoqiu Wen, Xiaofeng Zhu
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on real datasets verify the effectiveness of the proposed method with respect to node classification tasks, compared to SOTA methods. In this section, we conduct extensive experiments on 4 public benchmark datasets to evaluate the effectiveness of our method, compared to 12 comparison methods, on the node classification. |
| Researcher Affiliation | Academia | 1Guangxi Key Lab of Multisource Information Mining Security, Guangxi Normal University, Guilin, China 2School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China 3Information Systems Technology Design Pillar, Singapore University of Technology and Design, Singapore |
| Pseudocode | No | The paper describes the method using mathematical equations and descriptive text but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the described methodology, nor does it provide any links to a code repository. |
| Open Datasets | Yes | Datasets. The used datasets include two citation multiplex graph datasets (Wang et al. 2019), i.e., ACM and DBLP, and two movie multiplex graph datasets (Mo et al. 2023a), i.e., IMDB and Freebase. |
| Dataset Splits | No | The paper mentions evaluating the method on node classification tasks and reports Macro-F1 and Micro-F1 scores, but it does not specify any training, validation, or test dataset splits, percentages, or methodology for generating these splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, such as programming languages, libraries, or frameworks used in the implementation. |
| Experiment Setup | No | The paper discusses evaluating the effectiveness of the proposed method on node classification tasks and compares it to other methods, but it does not provide specific experimental setup details like hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |