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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Initializing Then Refining: A Simple Graph Attribute Imputation Network
Authors: Wenxuan Tu, Sihang Zhou, Xinwang Liu, Yue Liu, Zhiping Cai, En Zhu, Changwang Zhang, Jieren Cheng
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four benchmark datasets verify the superiority of ITR against state-of-the-art methods. |
| Researcher Affiliation | Collaboration | Wenxuan Tu1 , Sihang Zhou1 , Xinwang Liu1 , Yue Liu1 , Zhiping Cai1 , En Zhu1 , Changwang Zhang2 and Jieren Cheng3 1National University of Defense Technology, Changsha, China 2Tencent Technology, Shenzhen, China 3Hainan University, Haikou, China |
| Pseudocode | No | The paper includes mathematical equations and a framework diagram, but it does not contain a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | No | The paper does not contain any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We conduct experiments to evaluate the proposed ITR on four benchmark datasets, including Cora [Mc Callum et al., 2000], Citeseer [Sen et al., 2008], Amazon Computer (Amac), and Amazon Photo (Amap) [Shchur et al., 2018]. |
| Dataset Splits | Yes | Specifically, 1) in the profiling task, we randomly sample 40% nodes with attributes as the training set, and manually mask all attributes of the rest of 10% and 50% nodes (i.e., attribute-missing samples) as the validation set and the test set, respectively. ... 2) in the node classification task, the restored attributes are randomly split into 80% and 20% for training and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using the Adam optimization algorithm and a GCN-based framework but does not specify the versions of any software libraries or dependencies used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We employ a symmetric backbone framework consisting of 4-layer GCNs and optimize it with the Adam optimization algorithm, where we set the learning rate to 1e-3. Moreover, the learning rate, the latent dimension, the dropout rate, and the weight decay are set to 1e-3, 64, 0.5, and 5e-4, respectively. |