Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Authors: Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct controlled synthetic experiments to directly justify our theoretical conclusions. Comprehensive experiments on two real-world network datasets show consistent results in the analyzed setting of direct-transfering, while those on large-scale knowledge graphs show promising results in the more practical setting of transfering with fine-tuning.1 |
| Researcher Affiliation | Academia | 1University of Illinois Urbana-Champaign, 2Emory University, 3University of Washington, 4Georgia Institute of Technology |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present any structured algorithmic blocks. |
| Open Source Code | Yes | Code and processed data are available at https://github.com/Gentle Zhu/EGI. |
| Open Datasets | Yes | We use two real-world network datasets with role-based node labels: (1) Airport [45] contains three networks from different regions Brazil, USA and Europe. ... (2) Gene [68] contains the gene interactions regarding 50 different cancers. ... The source graph contains a cleaned full dump of 579K entities from YAGO [49] |
| Dataset Splits | No | The paper describes using source and target graphs for training and testing, and mentions concepts like 'pre-training' and 'fine-tuning'. However, it does not provide specific details on train/validation/test splits, such as percentages, sample counts, or references to predefined splits within the datasets used. |
| Hardware Specification | Yes | Our experiments were run on an AWS g4dn.2xlarge machine with 1 Nvidia T4 GPU. |
| Software Dependencies | No | The paper mentions software components like 'Adam as optimizer' and GNN encoders like 'GIN' and 'GCN', but it does not specify any version numbers for these software dependencies (e.g., 'Adam 1.0' or 'PyTorch 1.9'). |
| Experiment Setup | Yes | The main hyperparameter k is set 2 in EGI as a common practice. We use Adam [27] as optimizer and learning rate is 0.01. All baselines are set with the default parameters. The GNN parameters are frozen during the MLP training. |