Multi-Class Imbalanced Graph Convolutional Network Learning
Authors: Min Shi, Yufei Tang, Xingquan Zhu, David Wilson, Jianxun Liu
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real-world imbalanced graphs demonstrate that DR-GCN outperforms the state-of-the-art methods in node classification, graph clustering, and visualization. |
| Researcher Affiliation | Academia | 1Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, USA 2School of Computer Science and Engineering, Hunan University of Science and Technology, China |
| Pseudocode | Yes | Algorithm 1: Training the DR-GCN model |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We use four widely-used benchmark graph datasets [Wu et al., 2020], including Cora, Citeseer, Pubmed, and DBLP. |
| Dataset Splits | Yes | The remaining nodes are split into validation and testing sets where 10% are used for hyperparameter optimization, and 90% are used for testing respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | For GCN-based methods, we set the hidden embedding size r as 10, the dropout rate as 0.3, the L2 norm regularization weight decay as 0.03 and the learning rate for the gradient decent algorithm as 0.002. We set the maximum training epoch I as 1000 with an early stopping of 200. In our approach, the default values for M, N and α are set as 1, |Vl|/2 and 0.7, where |Vl| is the total number of labeled nodes. |