Summary of Collaborate to Adapt: Source-free Graph Domain Adaptation Via Bi-directional Adaptation, by Zhen Zhang et al.
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
by Zhen Zhang, Meihan Liu, Anhui Wang, Hongyang Chen, Zhao Li, Jiajun Bu, Bingsheng He
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Unsupervised Graph Domain Adaptation (UGDA) has been shown to be a practical solution for transferring knowledge from a label-rich source graph to an unlabelled target graph. However, most existing methods require access to the labelled source graph, which may not always be feasible due to privacy and regulatory concerns. In this paper, we explore the scenario of source-free unsupervised graph domain adaptation, where we aim to adapt models and graphs without accessing the labelled source graph. Specifically, we propose GraphCTA, a novel paradigm that combines model adaptation and graph adaptation through a series of procedures. Our approach involves three stages: (1) model adaptation based on node neighborhood predictions in the target graph, considering both local and global information; (2) graph adaptation by updating the graph structure and node attributes via neighborhood contrastive learning; and (3) using the updated graph as input to facilitate subsequent iterations of model adaptation. Our experimental results demonstrate that our proposed model outperforms recent source-free baselines by large margins on various public datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a map that shows where something is located, but it’s not very detailed. You want to make the map more accurate, but you don’t have any information about what the places are called or where they are in relation to each other. This is a problem that scientists and engineers face when trying to adapt models from one area of study to another without having any additional information. In this paper, we explore ways to solve this problem by proposing a new method called GraphCTA. Our approach involves three steps: (1) guessing what the places might be like based on how they are related to each other; (2) updating the map to make it more accurate and detailed; and (3) using the updated map to help us make better guesses about the places in the future. We tested our method on several different datasets and found that it outperformed existing methods by a significant margin. |
Keywords
* Artificial intelligence * Domain adaptation * Unsupervised
