Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
Authors: Xinyi Wu, Zhengdao Chen, William Wei Wang, Ali Jadbabaie
ICLR 2023 | Venue PDF | LLM Run Details | Input Tokens: 26,133 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,935 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we support our theoretical results with numerical experiments, which further suggest that the oversmoothing phenomenon observed in practice can be magnified by the difficulty of optimizing deep GNN models. |
| Researcher Affiliation | Academia | Xinyi Wu1, Zhengdao Chen2 , William Wang1, Ali Jadbabaie1 1Laboratory for Information and Decision Systems (LIDS), MIT 2Courant Institute of Mathematical Sciences, New York University |
| Pseudocode | No | The paper presents theoretical analysis, lemmas, theorems, and experimental results, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using PyTorch and PyTorch Geometric for implementation but does not provide any link to the source code for the methodology described in the paper or explicitly state that their code is open-source. |
| Open Datasets | Yes | We revisit the multi-class node classification task on the three widely used benchmark datasets: Cora, Cite Seer and Pub Med (Yang et al., 2016). |
| Dataset Splits | Yes | We used 60%/20%/20% random splits and ran GNN and APPNP with α = 0.1. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU or CPU models, memory) used for running the experiments. It only mentions the software frameworks used. |
| Software Dependencies | No | The paper states: 'All models were implemented with Py Torch (Paszke et al., 2019) and Py Torch Geometric (Fey & Lenssen, 2019).' However, it does not provide specific version numbers for these software dependencies or other ancillary software components. |
| Experiment Setup | Yes | In all cases we use the Adam optimizer and tune some hyperparameters for better performance. The hyperparameters used are summarized as follows. |