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..
Dynamic Group Link Prediction in Continuous-Time Interaction Network
Authors: Shijie Luo, He Li, Jianbin Huang
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on various datasets with and without unseen nodes show that CTGLP outperforms the state-of-the-art methods by 13.4% and 13.2% on average. |
| Researcher Affiliation | Academia | Shijie Luo , He Li and Jianbin Huang Xidian University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Training of CTGLP |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | Movie Lens-100K (ML100K for short) [Harper and Konstan, 2015] and Movie Lens-25M (ML25M for short) [Harper and Konstan, 2015] contains rating data from users on movies. Ciao DVD [Guo et al., 2014] consists of DVD rating data. |
| Dataset Splits | Yes | For each dataset, we split it into 8:1:1 for training, validation and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory. |
| Software Dependencies | Yes | We implement our CTGLP with Py Torch 1.6.0 and adopt the SGD as the optimizer. |
| Experiment Setup | Yes | The dimension D of initial embeddings, the dimension d of hidden states and the dimension s of group vectors are all tested in {16, 32, 64, 128, 256, 512}. The batch size and learning rate are searched in {32, 64, 128, 256} and {0.005, 0.01, 0.05, 0.1} respectively. Two convolutional layers are employed in CTGNN, and the neighbor sampling sizes are empirically set to 25 and 10 respectively. |