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].
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. |