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..
GPEN: Global Position Encoding Network for Enhanced Subgraph Representation Learning
Authors: Nannan Wu, Yuming Huang, Yiming Zhao, Jie Chen, Wenjun Wang
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on eight public datasets identify that GPEN significantly outperforms state-of-the-art methods in subgraph representation learning. ... 5. Experimental Evaluation |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2College of Management and Economics, Tianjin University, Tianjin, China 3Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, China. Correspondence to: Nannan Wu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Global Position Encoding Input: Graph G = (V, E) Output: Global Position Encodings {pv}v V 1: R Eq.(3) 2: wij R[i] + R[j], (i, j) E 3: r arg maxv V R[v] 4: G (V, E, W), W = {wij} 5: T Eq.(6) 6: for all v V do 7: tv dist T (v, r) 8: pv one hot(tv) 9: end for{pv}v V |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We use the same four real-world datasets and four synthetic datasets (Alsentzer et al., 2020b; Wang & Zhang, 2022; Kim & Oh, 2024). Detailed information about these datasets is presented in Table 2 and Appendix A.2.1. |
| Dataset Splits | Yes | The datasets are divided according to the split ratios outlined in the baselines (Alsentzer et al., 2020b; Wang & Zhang, 2022; Jacob et al., 2023; Kim & Oh, 2024). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions calculating vector differences using a COO-formatted sparse adjacency matrix, but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | we set the number of iterations t for Page Rank to 100, with the damping factor a set to 0.85. Similar to GLASS and Sub GNN, our model pre-trains nodes to generate node features for real-world datasets. Additionally, global position encoding is added as the initial feature for all datasets. We calculate the vector differences of node representations using a COO-formatted sparse adjacency matrix A, which significantly reduces memory usage. We use classic loss functions for classification tasks: BCE loss for binary classification and cross-entropy loss for multi-class classification. ... The balance factor controls the integration of local structural features and global position information during boundary-aware convolution. As illustrated in Figure 4(a), we observe a consistent pattern across all datasets: optimal performance emerges when b falls within a moderate range, typically between 0.6 and 0.8. ... The threshold parameter c determines the minimum size of connected components used for generating augmented samples. ... Smaller batch sizes generally lead to better performance... |