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..
Diffusion Probabilistic Models for Structured Node Classification
Authors: Hyosoon Jang, Seonghyun Park, Sangwoo Mo, Sungsoo Ahn
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively verify the superiority of our DPM-SNC in diverse scenarios, which include not only the transductive setting on partially labeled graphs but also the inductive setting and unlabeled graphs. |
| Researcher Affiliation | Academia | Hyosoon Jang1, Seonghyun Park1, Sangwoo Mo2, Sungsoo Ahn1 1POSTECH 2University of Michigan EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 DPM-SNC |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | In the transductive setting, we conduct experiments on seven benchmarks: Pubmed, Cora, and Citeseer [22]; Photo and Computer [23]; and Empire and Ratings [24]. |
| Dataset Splits | Yes | For all datasets, 20 nodes per class are used for training, and the remaining nodes are used for validation and testing.Then, we split 30%, 30%, and 40% of the entire nodes into training, validation, and test nodes. |
| Hardware Specification | Yes | For all experiments, we use a single GPU of NVIDIA Ge Force RTX 3090. |
| Software Dependencies | No | The paper does not provide specific software dependency versions (e.g., library or framework versions like PyTorch 1.9 or Python 3.8). |
| Experiment Setup | Yes | We search the learning rate within {1e 3, 5e 3, 1e 2} for all methods.For DPM-SNC, we fix the diffusion step to 100. We also set the size of the buffer to 50 and insert five samples into the buffer for every 30 training step. |