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..
Vertical Federated Graph Neural Network for Recommender System
Authors: Peihua Mai, Yan Pang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical studies show that Ver Fed GNN has competitive prediction accuracy with existing privacy preserving GNN frameworks while enhanced privacy protection for users interaction information. |
| Researcher Affiliation | Academia | 1Department of Analytics and Operations, National University of Singapore, 119077 Singapore. |
| Pseudocode | Yes | Algorithm 1 Federated Vertical Graph Neural Network |
| Open Source Code | Yes | Source code: https://github.com/maiph123/Vertical GNN |
| Open Datasets | Yes | Dataset: We use two benchmark datasets for recommendation, Movie Lens-1M2 (ML-1M) and Book Crossing3. For Book Crossing we randomly select 6000 users and 3000 items. The items are divided into non-overlapping groups to simulate the vertical federated setting. 2https://grouplens.org/datasets/movielens/1m/ 3http://www2.informatik.uni-freiburg.de/ cziegler/BX/ |
| Dataset Splits | Yes | Cross-validation is adopted to tune the hyper-parameter, where the training-validation-testing ratio is 60%-20%-20%. |
| Hardware Specification | Yes | The experiment is implemented on Ubuntu Linux 20.04 server with 16-core CPU and 64GB RAM, where the programming language is Python. |
| Software Dependencies | No | The paper mentions the operating system and programming language: "The experiment is implemented on Ubuntu Linux 20.04 server...programming language is Python." However, it does not specify version numbers for any critical software libraries or frameworks (e.g., PyTorch, TensorFlow, scikit-learn) that would be needed for reproducibility. |
| Experiment Setup | Yes | Implementation and Hyper-parameter Setting: Appendix H details the implementation and hyperparameters. Based on the hyper-parameter optimization, we set embedding dimension D to 6, layer size K to 2, learning rate η to 0.05, and neighbor threshold thd to 4 for ML-1M and 8 for Book Crossing. We use sigmoid as the activation function. We consider privacy parameter r from 2 to 50, inverse dimension reduction ratio Nu/q from 1 to 100, and participation rate from 0.2 to 1. The immediate gradients are clipped within [ 0.5, 0.5] so that g1 g2 1 before applying ternary quantization. |