Domain Adaptive Unfolded Graph Neural Networks
Authors: Zepeng Zhang, Olga Fink
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five real-world datasets demonstrate that the UGNNs integrated with CP outperform state-of-the-art GDA baselines. |
| Researcher Affiliation | Academia | Intelligent Maintenance and Operations Systems (IMOS) Lab Ecole Polytechnique F ed erale de Lausanne (EPFL), Lausanne, Switzerland EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and textual explanations, such as in "Proposed Methodology Design" and "Unfolded Graph Neural Networks" sections, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code https://github.com/zepengzhang/DAUGNN |
| Open Datasets | Yes | We conduct experiments on three citation networks, namely ACMv9 (A), Citationv1 (C), and DBLPv7 (D) (Zhang et al. 2021), and two social networks, namely Germany (DE) and England (EN) (Rozemberczki and Sarkar 2021). |
| Dataset Splits | Yes | We use 80% of labeled nodes in the source domain for training, 20% of the labeled nodes in the source domain for validation, and all the nodes in the unlabeled target domain for testing. |
| Hardware Specification | Yes | All the experiments are conducted on a Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions using the Adam optimizer (Kingma and Ba 2015) but does not provide specific version numbers for programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | The node representation dimension is set to 128, and the number of layers is set to 8. We use the Adam optimizer (Kingma and Ba 2015). We apply grid search for the learning rate and the weight decay parameter in the range of {1e-4, 5e-4,1e-3, 5e-3}. The trade-off parameter ξ for the MMD loss is searched in the range of {1,2,3,4,5}. For APPNP and GPRGNN, there is an additional teleport parameter α which is searched in the range of {0.1, 0.2, 0.5}. For Elastic GNN, there are two additional parameters λ1 and λ2 which are searched in the range of {3,6,9}. |