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..
Fisher Information Embedding for Node and Graph Learning
Authors: Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on several node classification benchmarks, we demonstrate that our proposed method outperforms existing attention-based graph models like GATs. |
| Researcher Affiliation | Academia | 1Department of Biosystems Science and Engineering, ETH Z urich, Switzerland 2SIB Swiss Institute of Bioinformatics, Switzerland. |
| Pseudocode | No | The paper describes the EM algorithm steps and mathematical formulations but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is available at https://github.com/Borgwardt Lab/ fisher_information_embedding. |
| Open Datasets | Yes | We assess the performance of our method with six widely used benchmark datasets for node classification, including Cora, Citeseer, Pubmed (Sen et al., 2008) as semi-supervised transductive learning datasets and Reddit (Hamilton et al., 2017), ogbn-arxiv (Hu et al., 2020), ogbn-products (Hu et al., 2020) as mediumor large-scale supervised learning datasets. |
| Dataset Splits | Yes | All results are computed from 10 runs using different random seeds with the optimal hyperparameters selected on the validation set. |
| Hardware Specification | Yes | All experiments were performed on a shared GPU and CPU cluster equipped with GTX1080 and TITAN RTX. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'Light GBM classifier', and 'FLAML' but does not specify their version numbers. |
| Experiment Setup | Yes | Full details on the datasets, experimental setup and implementation details can be found in the Appendix. The hyperparameters for training FIE models on different datasets are summarized in Table 3 and Table 4, respectively for unsupervised and supervised modes of FIE. For supervised learning tasks, a dropout with rate equal to 0.5 is used for training supervised embeddings of FIE. |