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..
Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
Authors: Sankalp Garg, Navodita Sharma, Woojeong Jin, Xiang Ren
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on five specially curated datasets for this problem and show a consistent improvement in time series prediction results. |
| Researcher Affiliation | Academia | Sankalp Garg1 , Navodita Sharma2 , Woojeong Jin3 and Xiang Ren3 1Indian Institute of Technology Delhi 2Indian Institute of Technology Madras 3University of Southern California EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model mathematically and textually, but it does not include any explicitly labeled 'Algorithm' or 'Pseudocode' blocks. |
| Open Source Code | Yes | We release the data and code of model DARTNET for future research1. 1https://github.com/INK-USC/DArtNet |
| Open Datasets | Yes | We release the data and code of model DARTNET for future research1. 1https://github.com/INK-USC/DArtNet |
| Dataset Splits | Yes | Dataset # Train # Valid # Test # Nodes # Rel # Granularity AGT 463,188 57,898 57,900 58 178 Monthly CAC(small) 2070 388 508 90 1 Yearly CAC(large) 116,933 167,047 334,096 20,000 1 Yearly MTG 270,362 39,654 74,730 44 90 Monthly AGG 3,879,878 554,268 1,108,538 6,635 246 Monthly |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions 'All models are implemented in Py Torch using Adam Optimizer for training.' but does not provide specific version numbers for PyTorch or other software dependencies. |
| Experiment Setup | Yes | All models are implemented in Py Torch using Adam Optimizer for training. The best hyperparameters are chosen using the validation dataset. Typically increasing value of λ gives better results, and the best results on each dataset are reported. ... In our experiments we use the functions as a single-layered feed-forward network. |