Generative Modelling of Structurally Constrained Graphs
Authors: Manuel Madeira, Clement Vignac, Dorina Thanou, Pascal Frossard
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 manuel.madeira@epfl.ch 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. |