Hyperbolic Graph Neural Networks
Authors: Qi Liu, Maximilian Nickel, Douwe Kiela
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that hyperbolic GNNs can lead to substantial improvements on various benchmark datasets. ... 4 Experiments |
| Researcher Affiliation | Industry | Qi Liu , Maximilian Nickel and Douwe Kiela Facebook AI Research {qiliu,maxn,dkiela}@fb.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data are available at https://github.com/facebookresearch/hgnn |
| Open Datasets | Yes | Hence, we instead use the much larger ZINC dataset [44, 24, 23], which has been used widely in graph generation for molecules using machine learning methods [25, 31]. ... A popular choice for this purpose is the QM9 dataset [37]. |
| Dataset Splits | Yes | The dataset consists of 250k examples in total, out of which we randomly sample 25k for the validation and test sets, respectively. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Deep Chem and RAMSGrad/AMSGrad, but does not provide specific version numbers for any key software components or libraries required for reproduction. |
| Experiment Setup | Yes | We use leaky Re LU as the activation function σ with the negative slope 0.5. We use RAMSGrad [4] and AMSGrad for hyperbolic parameters and Euclidean parameters, respectively. ... For Euclidean features x E, we first apply expx (x E) to map it into the Riemannian manifolds. To initialize embeddings E within the Riemannian manifold, we first uniformly sample from a range (e.g. [ 0.01, 0.01]) to obtain Euclidean embeddings... For Barabási-Albert graphs, we set the number of edges to attach from a new node to existing nodes to a random number between 1 and 100. For Erd os-Rényi, the probability for edge creation is set to 0.1 1. For Watts-Strogatz, each node is connected to 1 100 nearest neighbors in the ring topology, and the probability of rewiring each edge is set to 0.1 1. |