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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Watch Your Step: Learning Node Embeddings via Graph Attention
Authors: Sami Abu-El-Haija, Bryan Perozzi, Rami Al-Rfou, Alexander A. Alemi
NeurIPS 2018 | Venue PDF | 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 EMAIL Bryan Perozzi Google AI New York City, NY EMAIL Rami Al-Rfou Google AI Mountain View, CA EMAIL Alex Alemi Google AI Mountain View, CA EMAIL |
| 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. |