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..
DyRep: Learning Representations over Dynamic Graphs
Authors: Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that Dy Rep outperforms state-of-the-art baselines for dynamic link prediction and time prediction tasks and present extensive qualitative insights into our framework. 5 EXPERIMENTS |
| Researcher Affiliation | Collaboration | 1Georgia Institute of Technology 2Deep Mind |
| Pseudocode | Yes | Algorithm 1 Update Algorithm for S and A Algorithm 2 Computation of integral term in L for a mini-batch |
| Open Source Code | No | The paper does not include an explicit statement or a link providing concrete access to the source code for the described methodology. |
| Open Datasets | Yes | Social Evolution Dataset released by MIT Human Dynamics Lab #nodes: 83, #Initial Associations: 376, #Final Associations: 791, #Communications: 2016339 and Clustering Coefficient: 0.548. Github Dataset available at Github Archive #nodes: 12328, #Initial Associations: 70640, #Final Associations: 166565, #Communications: 604649 and Clustering Coefficient: 0.087. |
| Dataset Splits | No | The paper describes a train/test split for evaluation ('70/30 (train/test) split' and '65/35 (train/test) split') but does not explicitly mention specific validation dataset splits or percentages for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models, processor types, or memory amounts) used for running its main experiments. It only vaguely refers to 'Our machine' in Appendix G.2 for t-SNE visualization setup, which is not the experimental hardware for training. |
| Software Dependencies | No | The paper mentions programming languages and frameworks used for baselines (e.g., 'C++', 'Tensorflow', 'Python', 'Keras with Theano backend') but does not specify exact version numbers for these software dependencies or for their own implementation. |
| Experiment Setup | Yes | For social dataset: Num nodes = 100, Num Dynamics = 2, bptt (sequence length) = 200, embed_size = 32, hidden_unit_size = 32, nsamples (for survival) = 5, gradient_clip = 100 and no dropout. For github dataset: Num nodes = 12328, Num Dynamics = 2, bptt (sequence length) = 300, embed_size = 256, hidden_unit_size = 256, nsamples (for survival) = 5, gradient_clip = 100. |