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..
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Authors: Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, Hyunwoo J. Kim
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments In this section, we evaluate the benefits of our method against state-of-the-art models on link prediction benchmarks. Then we analyze the contribution of each component in Neo-GNNs and show how Neo-GNNs can actually generalize and learn neighborhood overlap-based heuristic methods. 4.1 Experiment Settings Datasets. We evaluate the effectiveness of our Neo-GNNs for link prediction on Open Graph Benchmark datasets [41] (OGB) : OGB-PPA, OGB-Collab, OGB-DDI, OGB-Citation2. |
| Researcher Affiliation | Academia | Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang , Hyunwoo J. Kim Department of Computer Science and Engineering Korea University EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper mentions using implementations from 'Py Torch Geometric [44]' and 'the official github repository for SEAL', but does not state that the code for Neo-GNNs is open-source or provide a link. |
| Open Datasets | Yes | We evaluate the effectiveness of our Neo-GNNs for link prediction on Open Graph Benchmark datasets [41] (OGB) : OGB-PPA, OGB-Collab, OGB-DDI, OGB-Citation2. |
| Dataset Splits | Yes | Table 1: Split ratio OGB-PPA 70/20/10, OGB-COLLAB 92/4/4, OGB-DDI 80/10/10, OGB-CITATION2 98/1/1 |
| Hardware Specification | Yes | The experiments are conducted on a RTX 3090 (24GB) and a Quadro RTX (48GB). |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Py Torch Geometric [44]' for implementations but does not specify their version numbers. |
| Experiment Setup | Yes | We set the number of layers to 3 and latent dimensionality to 256 for all GNN-based models. To train our method, we used GCN as a feature-based GNN based model and all MLP models in our Neo-GNNs consist of 2 fully connected layers. We jointly trained feature-based GNNs and Neo-GNNs. |