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 Knowledge Graph Reasoning with Historical Contrastive Learning
Authors: Yi Xu, Junjie Ou, Hui Xu, Luoyi Fu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed model on five benchmark graphs. The results demonstrate that CENET significantly outperforms all existing methods in most metrics, achieving at least 8.3% relative improvement of Hits@1 over previous state-of-the-art baselines on event-based datasets. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering Shanghai Jiao Tong University |
| Pseudocode | Yes | Algorithm 1: Learning algorithm of CENET |
| Open Source Code | Yes | All our datasets and codes are publicly available1. 1https://github.com/xyjigsaw/CENET |
| Open Datasets | Yes | All our datasets and codes are publicly available1. 1https://github.com/xyjigsaw/CENET |
| Dataset Splits | Yes | All datasets except ICEWS14 are split into training set (80%), validation set (10%), and testing set (10%). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | As to model configurations, we set the batch size to 1024, embedding dimension to 200, learning rate to 0.001, and use Adam optimizer. The training epoch for L is limited to 30, and the epoch for the second stage of contrastive learning is limited to 20. The value of hyperparameter α is set to 0.2, and λ is set to 2. |