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 [1].
Temporal Inductive Logic Reasoning over Hypergraphs
Authors: Yuan Yang, Siheng Xiong, Ali Payani, James C. Kerce, Faramarz Fekri
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on these benchmarks demonstrate that TILR achieves superior reasoning capability over various strong baselines. We create and release two temporal hypergraph datasets You Cook2-HG and nu Scenes-HG here, which is created from You Cook2 cooking recipe dataset and nu Scenes autonomous driving dataset [Caesar et al., 2020]. Experiments on these benchmarks demonstrate that TILR achieves superior reasoning capability over various strong baselines. |
| Researcher Affiliation | Collaboration | Yuan Yang1 , Siheng Xiong1 , Ali Payani2 , James C. Kerce1 and Faramarz Fekri1 1Georgia Institute of Technology 2Cisco {yyang754@, sxiong45@, clayton.kerce@gtri., faramarz.fekri@ece.}gatech.edu, EMAIL |
| Pseudocode | Yes | Algorithm 1: Multi-start Random B-walk and Algorithm 2: Path-consistency for temporal relation generalization. |
| Open Source Code | No | The paper states: "We release two novel temporal hypergraph datasets You Cook2-HG and nu Scenes-HG here", referring to datasets, but no explicit statement or link is provided for the open-source code of the methodology itself. |
| Open Datasets | Yes | We create and release two temporal hypergraph datasets You Cook2-HG and nu Scenes-HG here, which is created from You Cook2 cooking recipe dataset and nu Scenes autonomous driving dataset [Caesar et al., 2020]. |
| Dataset Splits | No | The paper mentions a "training split" and evaluates performance, but it does not provide specific percentages, counts, or a detailed methodology for how the datasets were split into training, validation, and test sets for reproducibility. |
| Hardware Specification | Yes | All experiments are done on a PC with i7-8700K and one GTX1080ti. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) used for the experiments. |
| Experiment Setup | No | The paper describes the different modes of TILR and the loss function used for training, but it does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |