Discrete-state Continuous-time Diffusion for Graph Generation
Authors: Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Menghai Pan, Zhichen Zeng, Mahashweta Das, Hanghang Tong
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on plain and molecule graphs show that DISCO can obtain competitive or superior performance against state-of-the-art graph generative models and provide additional sampling flexibility. |
| Researcher Affiliation | Collaboration | University of Illinois Urbana-Champaign. {zhexu3, rq5, zhichenz, htong}@illinois.edu Visa Research. {yuzchen, hchen, xirafan, menpan, mahdas}@visa.com |
| Pseudocode | Yes | Algorithm 1 Training of DISCO and Algorithm 2 τ-Leaping Graph Generation are provided. |
| Open Source Code | Yes | Our code is released 3. https://github.com/pricexu/Disco |
| Open Datasets | Yes | Datasets SBM, Planar [51], and Community [82] are used... The datasets QM9 [62], MOSES [58], and Guaca Mol [6] are chosen. |
| Dataset Splits | Yes | We follow the settings of SPECTRE [51] and Di Gress [73] to split the SBM, Planar [51], and Community [82] datasets into 64/16/20% for training/validation/test set. |
| Hardware Specification | Yes | All the efficiency study results are from one NVIDIA Tesla V100 SXM2-32GB GPU on a server with 96 Intel(R) Xeon(R) Gold 6240R CPU @ 2.40GHz processors and 1.5T RAM. |
| Software Dependencies | No | The paper mentions implementing DISCO in Py Torch and Py Torch-geometric but does not provide specific version numbers for these software dependencies in the text. |
| Experiment Setup | Yes | For both variants, the dropout is set as 0.1, the learning rate is set as 2e 4, and the weight decay is set as 0. |