Summary of Mldgg: Meta-learning For Domain Generalization on Graphs, by Qin Tian et al.
MLDGG: Meta-Learning for Domain Generalization on Graphs
by Qin Tian, Chen Zhao, Minglai Shao, Wenjun Wang, Yujie Lin, Dong Li
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel framework called MLDGG for domain generalization on graphs, aiming to develop models with robust generalization capabilities. The proposed method integrates cross-multi-domain meta-learning with structure learning and semantic identification to achieve adaptable generalization across diverse domains. Specifically, it introduces a generalized structure learner to mitigate the effects of task-unrelated edges and a representation learner to disentangle domain-invariant semantic and domain-specific variation information in node embedding. The framework is optimized using meta-parameters for both learners, facilitating knowledge transfer and enabling effective adaptation to graphs through fine-tuning within target domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make computer programs better at working with different kinds of data by sharing knowledge between similar datasets. It proposes a new way to do this called MLDGG that combines three ideas: learning the structure of the data, identifying what’s important in the data, and using this information to help the program learn from one dataset and apply it to another. |
Keywords
» Artificial intelligence » Domain generalization » Embedding » Fine tuning » Generalization » Meta learning
