Neural Trees for Learning on Graphs
Authors: Rajat Talak, Siyi Hu, Lisa Peng, Luca Carlone
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {talak, siyi, lisapeng, lcarlone}@mit.edu |
| 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. |