Hyperbolic Graph Convolutional Neural Networks
Authors: Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that HGCN learns embeddings that preserve hierarchical structure, and leads to improved performance when compared to Euclidean analogs, even with very low dimensional embeddings: compared to state-of-the-art GCNs, HGCN achieves an error reduction of up to 63.1% in ROC AUC for link prediction and of up to 47.5% in F1 score for node classification, also improving state-of-the art on the Pubmed dataset. |
| Researcher Affiliation | Academia | Department of Computer Science, Stanford University Institute for Computational and Mathematical Engineering, Stanford University {chami, rexying, chrismre, jure}@cs.stanford.edu |
| Pseudocode | No | The paper describes algorithms and operations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project website with code and data: http://snap.stanford.edu/hgcn |
| Open Datasets | Yes | CORA [36] and PUBMED [27] are standard benchmarks describing citation networks... Disease propagation tree. We simulate the SIR disease spreading model [2]... Protein-protein interactions (PPI) networks. PPI is a dataset of human PPI networks [37]... AIRPORT is a transductive dataset where nodes represent airports and edges represent the airline routes as from Open Flights.org. |
| Dataset Splits | Yes | In transductive LP tasks, we randomly split edges into 85/5/10% for training, validation and test sets. For transductive NC, we use 70/15/15% splits for AIRPORT, 30/10/60% splits for DISEASE, and we use standard splits [21, 46] with 20 train examples per class for CORA and PUBMED. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments (e.g., CPU/GPU models, memory). |
| Software Dependencies | No | The paper mentions optimizers like Adam and Riemannian SGD but does not specify software dependencies with version numbers (e.g., Python, PyTorch, specific library versions). |
| Experiment Setup | Yes | For all methods, we perform a hyper-parameter search on a validation set over initial learning rate, weight decay, dropout4, number of layers, and activation functions... We optimize all models with Adam [19], except Poincar e embeddings which are optimized with Riemannian SGD [4, 48]. |