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..
Toward a Manifold-Preserving Temporal Graph Network in Hyperbolic Space
Authors: Viet Quan Le, Viet Cuong Ta
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By evaluating on diverse real-world dynamic graphs, our model has achieved significant improvements in link prediction and new link prediction tasks, in comparison with other baselines. |
| Researcher Affiliation | Academia | Viet Quan Le and Viet Cuong Ta Human Machine Interaction Laboratory, VNU University of Engineering and Technology, Hanoi, Vietnam {EMAIL, EMAIL} |
| Pseudocode | Yes | Algorithm 1 HMPTGN learning process |
| Open Source Code | Yes | Our implementation is available at the github repository https://github.com/quanlv9211/HMPTGN. |
| Open Datasets | Yes | We evaluate our model and other baselines on 6 datasets: email communication networks Enron [Klimt and Yang, 2004]; academic co-author networks (COLAB) [Yang and Leskovec, 2012]; private messaging network system among students (UCI) [Panzarasa et al., 2009]; synthetic dataset based on the SIR disease spreading model (Disease) [Bjørnstad et al., 2002]; interactions network on the Math Overflow website (MO) [Paranjape et al., 2016]; social network graph of Facebook Wall posts (FB) [Yang et al., 2021]. |
| Dataset Splits | No | We choose the last k snapshots as the test set and the rest as the training set. The paper does not explicitly mention a separate validation set split or its details. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) in the main text. |