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..
Improving Temporal Link Prediction via Temporal Walk Matrix Projection
Authors: Xiaodong Lu, Leilei Sun, Tongyu Zhu, Weifeng Lv
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on 13 benchmark datasets verify the effectiveness and efficiency of TPNet, where TPNet outperforms other baselines on most datasets and achieves a maximum speedup of 33.3 compared to the SOTA baseline. |
| Researcher Affiliation | Academia | Xiaodong Lu CCSE Lab, Beihang University Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Node Representation Maintaining (G,λ,k,n,d R) |
| Open Source Code | Yes | Our code can be found at https://github.com/lxd99/TPNet. |
| Open Datasets | Yes | Experiments are conducted on the following 13 benchmark datasets collected by [16]. |
| Dataset Splits | Yes | For dataset splitting, we chronologically split each dataset with 70%/15%/15% for training/validating/testing. |
| Hardware Specification | Yes | Experiments are conducted on a Ubuntu server, whose CPU and GPU devices are one Intel(R) Xeon(R) Gold 6226R CPU @ 2.9GHz with 64 CPU cores and four Ge Force RTX 3090 GPUs with 24 GB memory respectively. |
| Software Dependencies | No | The paper mentions software like 'Dy GLib' and 'pytorch' but does not provide specific version numbers for these or other software dependencies required for replication. |
| Experiment Setup | Yes | For TPNet, the layer l of node representations, the number of recent interactions m, and dimension d R of the node representations are set to 3, 20 and 10 log(2E), where E is the number of the interactions. We find the best time decay weight λ via grid search within a range of 10 4 to 10 7. |