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..
Generative Modelling of Structurally Constrained Graphs
Authors: Manuel Madeira, Clement Vignac, Dorina Thanou, Pascal Frossard
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Con Struct demonstrates versatility across several structural and edge-deletion invariant constraints and achieves state-of-the-art performance for both synthetic benchmarks and attributed real-world datasets. For example, by incorporating planarity constraints in digital pathology graph datasets, the proposed method outperforms existing baselines, improving data validity by up to 71.1 percentage points. |
| Researcher Affiliation | Academia | Manuel Madeira EPFL, Lausanne, Switzerland EMAIL Clément Vignac EPFL, Lausanne, Switzerland Dorina Thanou EPFL, Lausanne, Switzerland Pascal Frossard EPFL, Lausanne, Switzerland |
| Pseudocode | Yes | Algorithm 1: Training Algorithm for Graph Discrete Diffusion Model Algorithm 2: Projector Algorithm 3: Sampling Algorithm for Constrained Graph Discrete Diffusion Model |
| Open Source Code | Yes | Our code and data are available at https://github.com/manuelmlmadeira/Con Struct. |
| Open Datasets | Yes | We focus on three synthetic datasets with different structural properties: the planar dataset [54], composed of planar and connected graphs; the tree dataset [7], composed of connected graphs without cycles (tree graph); and the lobster dataset [46]... We open-source both of them, representing to the best of our knowledge the first open-source digital pathology datasets specifically tailored for graph generation. |
| Dataset Splits | Yes | We follow the splits originally proposed for each of the datasets: 80% of the graphs are used in the training set and the remaining 20% are allocated to the test set. We use 20% of the train set as validation set. |
| Hardware Specification | Yes | All our experiments were run in a single Nvidia V100 32Gb GPUs. |
| Software Dependencies | No | The paper mentions "AMSGrad [65] version of Adam W [49]" as the optimizer, but does not provide version numbers for other key software components like Python, PyTorch, or specific libraries. |
| Experiment Setup | Yes | Regarding the optimizer, we used the AMSGrad [65] version of Adam W [49] with a learning rate of 0.0002 and weight decay of 1e-12 for all the experiments. ... λ is an hyperparameter that is tuned to balance both loss terms. |