Neural Common Neighbor with Completion for Link Prediction
Authors: Xiyuan Wang, Haotong Yang, Muhan Zhang
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we extensively evaluate the performance of both NCN and NCNC. Detailed experimental settings are included in Appendix D. |
| Researcher Affiliation | Academia | Xiyuan Wang1,2 wangxiyuan@pku.edu.cn Haotong Yang1,2,3 haotongyang@pku.edu.cn Muhan Zhang1 muhan@pku.edu.cn 1Institute for Artificial Intelligence, Peking University. 2School of Intelligence Science and Technology, Peking University. 3Key Lab of Machine Perception (Mo E) |
| Pseudocode | No | The paper describes its methods using mathematical equations and textual explanations, but it does not include a clearly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our code is available at https://github.com/Graph PKU/Neural Common Neighbor. |
| Open Datasets | Yes | We use seven popular real-world link prediction benchmarks. Among these, three are Planetoid citation networks: Cora, Citeseer, and Pubmed (Yang et al., 2016). Others are from Open Graph Benchmark (Hu et al., 2020): ogbl-collab, ogbl-ppa, ogbl-citation2, and ogbl-ddi. |
| Dataset Splits | Yes | Random splits use 70%/10%/20% edges for training/validation/test set respectively. |
| Hardware Specification | Yes | All experiments are conducted on an Nvidia 4090 GPU on a Linux server. |
| Software Dependencies | No | The paper mentions using 'Pytorch Geometric (Fey & Lenssen, 2019)', 'Pytorch (Paszke et al., 2019)', and 'optuna (Akiba et al., 2019)' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Training process. We utilize Adam optimizer to optimize models and set an epoch upper bound 100. All results of our models are provided from runs with 10 random seeds. |