Autoregressive Diffusion Model for Graph Generation
Authors: Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on six diverse generic graph datasets and two molecule datasets show that our model achieves better or comparable generation performance with previous state-of-the-art, and meanwhile enjoys fast generation speed. |
| Researcher Affiliation | Academia | 1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, USA. Correspondence to: Lingkai Kong <lkkong@gatech.edu>. |
| Pseudocode | Yes | The detailed training procedure is summarized in Algorithm 1 in Appendix. A.6. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of its own code. |
| Open Datasets | Yes | We evaluate the performance of GRAPHARM on six diverse graph generation benchmarks from different domains: (1) Community-small (You et al., 2018b), (2) Caveman (You, 2018), (3) Cora (Sen et al., 2008), (4) Breast (Gonzalez-Malerva et al., 2011), (5) Enzymes (Schomburg et al., 2004) and (6) Ego-small (Sen et al., 2008). For each dataset, we use 80% of the graphs as training set and the rest 20% as test sets. ... We use two molecular dataset, QM9 (Ramakrishnan et al., 2014) and ZINC250k (Irwin et al., 2012). |
| Dataset Splits | Yes | For each dataset, we use 80% of the graphs as training set and the rest 20% as test sets. Following (Liao et al., 2019), we randomly select 20% from the training data as the validation set. |
| Hardware Specification | No | The paper mentions 'funds/computing resources from Georgia Tech' but does not provide specific hardware details such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions using specific optimizers (ADAM) and network architectures (GAT, GNN) but does not provide specific version numbers for software dependencies or libraries like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | Model optimization: We use ADAM with β1 = 0.9 and β = 0.999 as the optimizer. The learning rate is set for 10 4 and 5 10 4 for the denoising network and diffusion ordering network respectively on all the datasets. ... We set the number of trajectories M as 4 for all the datasets. |