Exploring the Role of Node Diversity in Directed Graph Representation Learning
Authors: Jincheng Huang, Yujie Mo, Ping Hu, Xiaoshuang Shi, Shangbo Yuan, Zeyu Zhang, Xiaofeng Zhu
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on seven real-world datasets validate the superior performance of our method compared to state-of-the-art methods in terms of both node classification and link prediction tasks. |
| Researcher Affiliation | Academia | Jincheng Huang1 , Yujie Mo1 , Ping Hu1 , Xiaoshuang Shi1 , Shangbo Yuan3 , Zeyu Zhang2 , Xiaofeng Zhu1 1School of Computer Science and Engineering, University of Electronic Science and Technology of China 2Huazhong Agricultural University 3School of Engineering and Design, Technical University of Munich |
| Pseudocode | No | The paper includes a flowchart (Figure 3) but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the effectiveness of the proposed method on 2 homophilic datasets and 5 heterophilic datasets. Homophilic datasets include Cora-ML and Citeseer-Full [Bojchevski and G unnemann, 2018]. Heterophilic datasets include Chameleon, Squirrel [Pei et al., 2020], Roman-Empire [Platonov et al., 2023], Arxiv-Year [Leskovec et al., 2005], and Snap-Patents [Leskovec and Krevl, 2014]. |
| Dataset Splits | Yes | Specifically, for the node classification task, we split all datasets in Dir-GNN [Rossi et al., 2023] and the detail can be found in the Appendix. For directed graph link prediction task, we remove 10% of edges for testing, 5% for validation, and use the rest of the edges for training. |
| Hardware Specification | Yes | We conduct all experiments on a server with Nvidia RTX 4090 (24GB memory each). |
| Software Dependencies | No | The paper mentions optimizing parameters by Adam optimization but does not provide specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | In the proposed method, we optimize all parameters by the Adam optimization [Kingma and Ba, 2015] with the learning rate in the range of {0.005, 0.01} and set the weight decay as 0. Moreover, we set the number of model layers in the range of {4, 5, 6}, set the dropout in the range of {0.0, 0.35, 0.5, 0.6}, and set the size of the hidden unit in the range of {32, 128, 256}. We set α for preserving the representation of the previous layer in the range of {0.0, 0.3, 0.5, 0.8, 1.0}, and set λ in our regularization term in the range of {0.0, 0.1, 0.2, 0.9}. |