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..
Inductive Matrix Completion Based on Graph Neural Networks
Authors: Muhan Zhang, Yixin Chen
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare IGMC with state-of-the-art matrix completion algorithms on five benchmark datasets. Without using any content, IGMC achieves the smallest RMSEs on four of them, even beating many transductive baselines augmented by side information. |
| Researcher Affiliation | Collaboration | Muhan Zhang* Washington University in St. Louis EMAIL *Now at Facebook Yixin Chen Washington University in St. Louis EMAIL |
| Pseudocode | Yes | Algorithm 1 ENCLOSING SUBGRAPH EXTRACTION |
| Open Source Code | Yes | Our code is publicly available at https://github.com/muhanzhang/IGMC. |
| Open Datasets | Yes | We conduct experiments on five common matrix completion datasets: Flixster (Jamali & Ester, 2010), Douban (Ma et al., 2011), Yahoo Music (Dror et al., 2011), Movie Lens-100K and Movie Lens-1M (Miller et al., 2003). |
| Dataset Splits | Yes | For ML-100K, we train and evaluate on the canonical u1.base/u1.test train/test split. For ML-1M, we randomly split it into 90% and 10% train/test sets. For Flixster, Douban and Yahoo Music we use the preprocessed subsets and splits provided by (Monti et al., 2017). |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper states 'We implemented IGMC using pytorch geometric (Fey & Lenssen, 2019)' but does not provide specific version numbers for PyTorch Geometric or any other key software libraries. |
| Experiment Setup | Yes | The final architecture uses 4 R-GCN layers with 32, 32, 32, 32 hidden dimensions. Basis decomposition with 4 bases is used... The final MLP has 128 hidden units and a dropout rate of 0.5. We use 1-hop enclosing subgraphs... randomly drop out its adjacency matrix entries with a probability of 0.2... We set the λ in (7) to 0.001. We train our model using the Adam optimizer... with a batch size of 50 and an initial learning rate of 0.001, and multiply the learning rate by 0.1 every 20 epochs for ML-1M, and every 50 epochs for all other datasets. |