Diffusion Probabilistic Models for Structured Node Classification
Authors: Hyosoon Jang, Seonghyun Park, Sangwoo Mo, Sungsoo Ahn
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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 {hsjang1205,shpark26,sungsoo.ahn}@postech.ac.kr, swmo@umich.edu |
| 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. |