Rank and Align: Towards Effective Source-free Graph Domain Adaptation
Authors: Junyu Luo, Zhiping Xiao, Yifan Wang, Xiao Luo, Jingyang Yuan, Wei Ju, Langechuan Liu, Ming Zhang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed RNA. |
| Researcher Affiliation | Collaboration | Junyu Luo1 , Zhiping Xiao2 , Yifan Wang3 , Xiao Luo2 , Jingyang Yuan1 , Wei Ju1 , Langechuan Liu4 and Ming Zhang1 1 School of Computer Science, National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab, Peking University, Beijing 2 University of California, Los Angeles 3 University of International Business and Economics 4 Anker Innovations |
| Pseudocode | Yes | Algorithm 1 Optimization Algorithm of RNA |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | Yes | Datasets. We perform experiments with practical sourcefree domain adaptation settings and benchmark datasets. We test our RNA in cross-dataset and split-dataset scenarios. For biochemical datasets, e.g., Mutagenicity [Kazius et al., 2005], PROTEINS [Borgwardt et al., 2005], and FRANKENSTEIN [Orsini et al., 2015]. Furthermore, we test our method on the sub-datasets of the COX2 [Sutherland et al., 2003] and BZR [Sutherland et al., 2003] datasets. |
| Dataset Splits | No | The paper mentions using specific datasets but does not provide specific percentages or counts for train/validation/test splits. It also does not explicitly state how the data was partitioned for training and validation, or refer to standard predefined splits for its experimental setup. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions using a GCN encoder and Adam optimizer but does not provide specific version numbers for any software libraries or dependencies, such as Python, PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | Yes | For RNA, we encode the graph data with a 2-layer GCN encoder, with an embedding dimension of 128. We optimize the model with an Adam optimizer with a mini-batch of 128 and a learning rate of 0.001. The model is initialized with 100 epochs of pre-training on the source domain In domain adaptation, the harmonic set ratio is 40%. To reduce randomness, we perform 5 runs and report the average accuracy. |