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..
Neural Trees for Learning on Graphs
Authors: Rajat Talak, Siyi Hu, Lisa Peng, Luca Carlone
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the neural tree architecture for semi-supervised node classification in 3D scene graphs and citation networks (Section 7). Our experiments on 3D scene graphs demonstrate that neural trees outperform standard, local message passing GNNs, by a large margin. |
| Researcher Affiliation | Academia | Rajat Talak, Siyi Hu, Lisa Peng, and Luca Carlone Laboratory of Information and Decision Systems (LIDS) Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: H-tree |
| Open Source Code | Yes | Our code is publically available at https://github.com/MIT-SPARK/neural_tree |
| Open Datasets | Yes | We run semi-supervised node classification experiments on Stanford s 3D scene graph dataset [2]. [...] We use the popular citation network datasets [60] |
| Dataset Splits | Yes | We randomly select 10% of the nodes for validation and 20% for testing. The hyper-parameters of the two approaches are separately tuned based on the best validation accuracy, while using all 70% of the remaining nodes for training |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | No | The paper states that "The hyper-parameters of the two approaches are separately tuned based on the best validation accuracy, while using all 70% of the remaining nodes for training; see supplementary material for details." Since the details are deferred to supplementary material and not in the main text, it does not explicitly provide these details in the main paper. |