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..
Neural Common Neighbor with Completion for Link Prediction
Authors: Xiyuan Wang, Haotong Yang, Muhan Zhang
ICLR 2024 | Venue PDF | 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 EMAIL Haotong Yang1,2,3 EMAIL Muhan Zhang1 EMAIL 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. |