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..
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Authors: Cristian Bodnar, Francesco Di Giovanni, Benjamin Chamberlain, Pietro Lió, Michael Bronstein
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments. Synthetic experiments. We consider a simple setup given by a connected bipartite graph with equally sized partitions. ... Real-world experiments. We test our models on multiple real-world datasets [47, 53, 57, 61, 66] with an edge homophily coefficient h ranging from h = 0.11 (very heterophilic) to h = 0.81 (very homophilic). ... Results. From Table 1 we see that our models are first in 5/6 benchmarks with high heterophily (h < 0.3) and second-ranked on the remaining one (i.e. Chameleon). |
| Researcher Affiliation | Collaboration | Cristian Bodnar University of Cambridge EMAIL Francesco Di Giovanni Twitter EMAIL Benjamin P. Chamberlain Twitter Pietro Liò University of Cambridge Michael Bronstein University of Oxford & Twitter |
| Pseudocode | No | The paper describes methods through equations and textual explanations, but it does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/twitter-research/neural-sheaf-diffusion. |
| Open Datasets | Yes | We test our models on multiple real-world datasets [47, 53, 57, 61, 66] with an edge homophily coefficient h ranging from h = 0.11 (very heterophilic) to h = 0.81 (very homophilic). |
| Dataset Splits | Yes | Each split contains 48%/32%/20% of nodes per class for training, validation and testing, respectively. |
| Hardware Specification | Yes | All experiments are conducted on 8 NVIDIA A6000 GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' and 'Adam optimizer' but does not specify their version numbers. While it references a specific version for a Householder transformation utility (torch-householder Version: 1.0.1), it does not provide version numbers for the main software dependencies like PyTorch itself. |
| Experiment Setup | Yes | The models are trained for 200 epochs using the Adam optimizer [38] with a learning rate of 0.01 and a weight decay of 5e-4 for all datasets. We perform early stopping with a patience of 50 epochs on the validation accuracy. |