Summary of Rank and Align: Towards Effective Source-free Graph Domain Adaptation, by Junyu Luo et al.
Rank and Align: Towards Effective Source-free Graph Domain Adaptation
by Junyu Luo, Zhiping Xiao, Yifan Wang, Xiao Luo, Jingyang Yuan, Wei Ju, Langechuan Liu, Ming Zhang
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 A novel approach to source-free graph domain adaptation is introduced, leveraging graph neural networks (GNNs) to transfer knowledge from source models instead of graphs to a target domain. The Rank and Align (RNA) method ranks graph similarities using spectral seriation for robust semantics learning and aligns inharmonic graphs with harmonic graphs close to the source domain for subgraph extraction. To overcome label scarcity, spectral seriation infers pairwise rankings guiding semantic learning using a similarity learning objective. Spectral clustering and silhouette coefficient detect harmonic graphs, which source models can classify easily. Domain-invariant subgraphs are extracted from inharmonic graphs via adversarial edge sampling, guiding invariant GNN learning. Benchmark datasets demonstrate the effectiveness of RNA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to adapt graphs without needing all the original graph data is developed. This method uses special kinds of neural networks for graphs (GNNs) to transfer knowledge from one source model to a target domain. The approach, called Rank and Align (RNA), first ranks similar graphs based on their structure to learn robustly about what they mean. Then it aligns graphs that are different with those that are more like the original graph, making it easier for GNNs to understand them. To solve problems where there’s not enough label information, RNA uses a special algorithm to guess which pairs of graphs are most similar. It also finds patterns in the data to detect when domains are very different and adjusts accordingly. Tests on several datasets show that this approach works well. |
Keywords
» Artificial intelligence » Domain adaptation » Gnn » Semantics » Spectral clustering
