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].
Link Prediction Based on Graph Neural Networks
Authors: Muhan Zhang, Yixin Chen
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate SEAL. Our results show that SEAL is a superb and robust framework for link prediction, achieving unprecedentedly strong performance on various networks. |
| Researcher Affiliation | Academia | Muhan Zhang Department of CSE Washington University in St. Louis EMAIL Yixin Chen Department of CSE Washington University in St. Louis EMAIL |
| Pseudocode | No | The paper does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | The code and data are available at https://github.com/muhanzhang/SEAL. |
| Open Datasets | Yes | The code and data are available at https://github.com/muhanzhang/SEAL. ... Datasets The eight datasets used are: USAir, NS, PB, Yeast, C.ele, Power, Router, and E.coli (please see Appendix C for details). |
| Dataset Splits | Yes | We randomly remove 10% existing links from each dataset as positive testing data. ... We use the remaining 90% existing links as well as the same number of additionally sampled nonexistent links to construct the training data. ... If the second-order heuristic AA outperforms the first-order heuristic CN on 10% validation data, then we choose h = 2; otherwise we choose h = 1. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions: 'Thus, we choose a recent architecture DGCNN [17] as the default GNN, and node2vec [20] as the default embeddings.' However, no specific version numbers for these software components or other libraries are provided. |
| Experiment Setup | Yes | Here, we select h only from {1, 2}, since on one hand we empirically verified that the performance typically does not increase after h 3, which validates our theoretical results that the most useful information is within local structures. ... The selection principle is very simple: If the second-order heuristic AA outperforms the first-order heuristic CN on 10% validation data, then we choose h = 2; otherwise we choose h = 1. For datasets PB and E.coli, we consistently use h = 1 to fit into the memory. |