Watch Your Step: Learning Node Embeddings via Graph Attention
Authors: Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on link prediction tasks, as we aim to produce embeddings that best-preserve the graph structure, generalizing to unseen information. We improve state-of-the-art results on a comprehensive suite of real-world graph datasets including social, collaboration, and biological networks, where we observe that our graph attention model can reduce the error by up to 20%-40%. |
| Researcher Affiliation | Collaboration | Sami Abu-El-Haija Information Sciences Institute, University of Southern California haija@isi.edu Bryan Perozzi Google AI New York City, NY bperozzi@acm.org Rami Al-Rfou Google AI Mountain View, CA rmyeid@google.com Alex Alemi Google AI Mountain View, CA alemi@google.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | To ensure repeatability of results, we have released our model and instructions6. Available at http://sami.haija.org/graph/context |
| Open Datasets | Yes | Datasets available from SNAP https://snap.stanford.edu/data. PPI [33] (C. Stark, B. Breitkreutz, T. Reguly, L. Boucher, A. Breitkreutz, and M. Tyers. Biogrid: A general repository for interaction datasets. In Nucleic Acids Research, 2006.) |
| Dataset Splits | Yes | For classification, we follow the data splits of [37]. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'TensorFlow' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | For the results Table 1, we use β = 0.5, C = 10, and P(0) = diag(80), which corresponds to 80 walks per node. |