GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstruction
Authors: Shijin Duan, Ruyi Ding, Jiaxing He, Aidong Ding, Yunsi Fei, Xiaolin Xu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both theoretical analysis and numerical evaluations demonstrate that our methodology significantly outperforms existing self-correlation-based GAEs in graph structure reconstruction. |
| Researcher Affiliation | Academia | Shijin Duan Ruyi Ding Jiaxing He Aidong Adam Ding Yunsi Fei Xiaolin Xu Northeastern University {duan.s, ding.ruy, he.jiaxi, a.ding, y.fei, x.xu}@northeastern.edu |
| Pseudocode | No | The paper describes methods in detail but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available in https://github.com/sjduan/Graph Croc. |
| Open Datasets | Yes | Dataset We assess Graph Croc in various graph tasks. Specifically, we utilize datasets for molecule, scaling from small (PROTEINS [2]) to large (Protein-Protein Interactions (PPI) [12], and QM9 [29]), for scientific collaboration (COLLAB [46]), and for movie collaboration (IMDB-Binary [46]). |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details for a separate validation split (e.g., specific percentages or sample counts for a validation set). |
| Hardware Specification | Yes | For example, due to the large graph size, the default setting (vector dimension of 128 and layer number of 4) in EGNN when reproducing the PPI task will cause the out-of-memory issue on the 40GB A100 GPU. |
| Software Dependencies | No | The paper describes the implementation details and dependencies (e.g., GNN models, optimizers) but does not provide specific version numbers for any software libraries or packages. |
| Experiment Setup | Yes | Table 6: The architecture and training configuration of Graph Croc on selected graph tasks. input dim. embedding dim. # layers pooling rate training config. (opt., lr, epochs) |