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 [1].
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. |