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..
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling
Authors: Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical study shows that EDGE is much more efficient than competing methods and can generate large graphs with thousands of nodes. It also outperforms baseline models in generation quality: graphs generated by the proposed model have graph statistics more similar to those of training graphs. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Tufts University, Medford, MA, USA. Correspondence to: Xiaohui Chen <EMAIL>, Li-Ping Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Degree-guided graph generation |
| Open Source Code | Yes | The implementation of our model is available at github.com/tufts-ml/graph-generation-EDGE. |
| Open Datasets | Yes | Datasets. We conduct experiments on both generic graph datasets and large networks. ... Community and Ego datasets (You et al., 2018) ... Polblogs (Adamic & Glance, 2005), Cora (Sen et al., 2008), Road-Minnesota (Rossi & Ahmed, 2015), and PPI (Stark et al., 2010). ... QM9 dataset (Ramakrishnan et al., 2014). |
| Dataset Splits | Yes | We follow You et al. (2018) to generate the Community and Ego datasets and use the same data splitting strategy. ... For large network generation, we do not include validation/test sets in this task. |
| Hardware Specification | Yes | We train our models on Tesla A100, Tesla V100, or NVIDIA QUADRO RTX 6000 GPU and 32 CPU cores for all experiments. ... The sampling speed reported in Figure 3 of all baselines and our approach is tested on Tesla A100 GPU. |
| Software Dependencies | No | The paper mentions using Py Torch (Paszke et al., 2019) and Py Torch Geometric (Fey & Lenssen, 2019) but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | Table 8 provides hyperparameters for Diffusion (Diffusion steps T, Noise scheduling), Optimization (Learning rate, Optimizer, weight decay, Batch size, Number of epochs/iteration), and Architecture (Number of MPBs, Hidden dimension, Activation function, Dropout rate). |