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..
Mean-field theory of graph neural networks in graph partitioning
Authors: Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A theoretical performance analysis of the graph neural network (GNN) is presented. ... This demonstrates a good agreement with numerical experiments. |
| Researcher Affiliation | Academia | Tatsuro Kawamoto, Masashi Tsubaki Arti๏ฌcial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo, Japan EMAIL Tomoyuki Obuchi Department of Mathematical and Computing Science, Tokyo Institute of Technology, 2-12-1 Ookayama Meguro-ku Tokyo, Japan EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions implementing the GNN using Chainer but does not provide any statement or link indicating that their specific implementation code is open-source or publicly available. |
| Open Datasets | No | The paper uses data generated from the Stochastic Block Model (SBM) rather than a pre-existing, publicly available dataset with concrete access information. |
| Dataset Splits | Yes | For the validation (development) set, 100 graph instances of the same SBMs are provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | Yes | We implemented the GNN using Chainer (version 3.2.0) [36]. |
| Experiment Setup | Yes | We set the dimension of the feature space to D = 100 and the number of layers to T = 100, and each result represents the average over 30 samples. ... We also employ residual networks (Res Nets) [38] and batch normalization (BN) [39]. |