Graph Domain Adaptation via Theory-Grounded Spectral Regularization
Authors: Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | with numerical agreement with extensive real-world experiments: SS and MFR regularizations bring more benefits to the scenarios of node transfer and link transfer, respectively. |
| Researcher Affiliation | Academia | Yuning You1, Tianlong Chen2, Zhangyang Wang2, Yang Shen1 1Department of Electrical and Computer Engineering, Texas A&M University 2Department of Electrical and Computer Engineering, University of Texas at Austin |
| Pseudocode | No | The paper contains mathematical derivations and equations but no explicit pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Codes are released at https://github.com/Shen-Lab/GDA-SpecReg. |
| Open Datasets | Yes | We utilize protein sequences together with freely accessible computational PPIs via whole-genome comparisons (Szklarczyk et al., 2021) to predict experimental PPIs |
| Dataset Splits | Yes | In training, we hold out 20% of human PPIs for validation. |
| Hardware Specification | Yes | Experiments are distributed on computer clusters with Tesla K80 GPU (11 GB memory) and NVIDIA A100 GPU (40 GB memory). |
| Software Dependencies | No | The paper mentions software components like 'Sinkhorn Transformer', 'GIN', 'PyTorch APIs', and 'MATLAB code' but does not specify their version numbers. |
| Experiment Setup | Yes | We train with convergence assured for 500 epochs with learning rate 0.0001, hidden dimension 256 and batch size 128 which is sampled by random walk, optimized by Adam optimizer. |