Unsupervised Network Embedding Beyond Homophily
Authors: Zhiqiang Zhong, Guadalupe Gonzalez, Daniele Grattarola, Jun Pang
TMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive empirical evaluations on both synthetic and real-world datasets with varying homophily ratios validate the effectiveness of SELENE in homophilous and heterophilous settings showing an up to 12.52% clustering accuracy gain. |
| Researcher Affiliation | Academia | Zhiqiang Zhong EMAIL University of Luxembourg Guadalupe Gonzalez EMAIL Imperial College London Daniele Grattarola EMAIL Università della Svizzera italiana Jun Pang EMAIL University of Luxembourg |
| Pseudocode | Yes | Algorithm 1: SELf-sup Ervised Network Embedding (SELENE) Framework |
| Open Source Code | Yes | Code and data are available at: https://github.com/zhiqiangzhongddu/SELENE |
| Open Datasets | Yes | All real-world datasets are available online1. https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html |
| Dataset Splits | No | The paper mentions evaluating on 'node clustering, node class prediction and link prediction' tasks and for node classification states it 'utilises node labels to train a classifier, after obtaining unsupervised node representations, to predict labels of test nodes'. However, it does not provide specific percentages, sample counts, or explicit methodology for how these splits were performed (e.g., 80/10/10 split, or a random seed for splitting). |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python 3.8, PyTorch 1.9). |
| Experiment Setup | No | The paper mentions hyperparameters like 'probability px, pe' for distortion and 'λ' for the Barlow-Twins loss, but it does not provide their specific values used for the main experimental results. For λ, it references 'default settings as Zbontar et al. (2021)' without stating these values. |