Sheaf Hypergraph Networks
Authors: Iulia Duta, Giulia Cassarà, Fabrizio Silvestri, Pietro Lió
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experimentation, we show that this generalization significantly improves performance, achieving top results on multiple benchmark datasets for hypergraph node classification. |
| Researcher Affiliation | Academia | Iulia Duta University of Cambridge id366@cam.ac.uk Giulia Cassarà University of Rome, La Sapienza giulia.cassara@uniroma1.it Fabrizio Silvestri University of Rome, La Sapienza fabrizio.silvestri@uniroma1.it Pietro Liò University of Cambridge pl219@cam.ac.uk |
| Pseudocode | No | The paper describes computational procedures but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct link or explicit statement for the availability of the source code for its own methodology. The only code link refers to a baseline method, HNHN: “Code available: https://github.com/twistedcubic/HNHN.” which is not their own work. |
| Open Datasets | Yes | We evaluate our model on eight real-world datasets that vary in domain, scale, and heterophily level and are commonly used for benchmarking hypergraphs. These include Cora, Citeseer, Pubmed, Cora-CA, DBLP-CA [37], House [52], Senate and Congress [53]. |
| Dataset Splits | Yes | To ensure a fair comparison with the baselines, we follow the same training procedures used in [50] by randomly splitting the data into 50% training samples, 25% validation samples and 25% test samples, and running each model 10 times with different random splits. |
| Hardware Specification | Yes | The experiments are executed on a single NVIDIA Quadro RTX 8000 with 48GB of GPU memory. |
| Software Dependencies | No | The paper mentions “PyTorch” indirectly when discussing a fix for Hyper GCN code, but does not provide specific version numbers for PyTorch or any other software dependencies used in its experiments. |
| Experiment Setup | No | Details on all the model choices and hyper-parameters can be found in the Supplementary Material. |