Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Deformable Graph Convolutional Networks
Authors: Jinyoung Park, Sungdong Yoo, Jihwan Park, Hyunwoo J. Kim7949-7956
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate that Deformable GCNs flexibly handles the heterophily and achieve the best performance in node classification tasks on six heterophilic graph datasets. |
| Researcher Affiliation | Academia | Jinyoung Park, Sungdong Yoo, Jihwan Park, Hyunwoo J. Kim* Department of Computer Science and Engineering, Korea University EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/mlvlab/Deformable GCN. |
| Open Datasets | Yes | For validating our model, we use six heterophilic graph datasets and three homophilic graph datasets... For all datasets, we apply the splits (48%/ 32%/ 20%)1 of nodes per class for (train/ validation/ test) provided by (Pei et al. 2020) for a fair comparison as (Zhu et al. 2020). 1https://github.com/graphdml-uiuc-jlu/geom-gcn. |
| Dataset Splits | Yes | For all datasets, we apply the splits (48%/ 32%/ 20%)1 of nodes per class for (train/ validation/ test) provided by (Pei et al. 2020) for a fair comparison as (Zhu et al. 2020). 1https://github.com/graphdml-uiuc-jlu/geom-gcn. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify version numbers for any software dependencies or libraries (e.g., Python, PyTorch, CUDA). |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma and Ba 2014) with ℓ2-regularization and 500 epochs for training our model and the baselines. The performance is reported with the best model on the validation datasets. All experiments are repeated 10 times as (Pei et al. 2020; Zhu et al. 2020) and accuracy is used as an evaluation metric. |