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..
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
Authors: Qiyu Kang, Kai Zhao, Yang Song, Sijie Wang, Wee Peng Tay
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate that our approach adapts better to different types of graph datasets than popular state-of-the-art graph node embedding GNNs. |
| Researcher Affiliation | Collaboration | 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 2C3 AI, Singapore. |
| Pseudocode | Yes | Algorithm 1 Graph Node Embedding with Ham GNN |
| Open Source Code | Yes | The code is available at https://github.com/zknus/ Hamiltonian-GNN. |
| Open Datasets | Yes | We select datasets with various geometries including the three citation networks: Cora (Mc Callum et al., 2004), Citeseer (Sen et al., 2008), Pubmed (Namata et al., 2012); and two low hyperbolicity datasets (Chami et al., 2019): Disease and Airport (cf. Table 5). and In this section, to underscore our model s capacity for handling large graph datasets, we conduct a series of experiments on the Ogbn datasets obtained from https: //ogb.stanford.edu/docs/nodeprop/, in compliance with the experimental setup detailed in (Hu et al., 2021). |
| Dataset Splits | Yes | We use a 60%, 20%, 20% random split for training, validation, and test sets on this new dataset. |
| Hardware Specification | No | The paper discusses computational cost and time in Table 11, but does not provide specific hardware details such as CPU/GPU models, memory, or cluster specifications used for the experiments. |
| Software Dependencies | No | We employ the ODE solver (Chen, 2018) in the implementation of Ham GNN. For computation efficiency and performance effectiveness, the fixed-step explicit Euler solver (Chen et al., 2018a) is used in Ham GNN. |
| Experiment Setup | Yes | We use the ADAM optimizer (Kingma & Ba, 2014) with the weight decay as 0.001. We set the learning rate as 0.01 for citation networks and 0.001 for Disease and Airport datasets. The results presented in Table 1 are under the 3 layers Ham GNN setting. Ham GNN first compresses the dimension of input features to the fixed hidden dimension (e.g. 64) through a fully connected (FC) layer. |