Summary of Graphlora: Structure-aware Contrastive Low-rank Adaptation For Cross-graph Transfer Learning, by Zhe-rui Yang et al.
GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning
by Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, Hao Liu
First submitted to arxiv on: 25 Sep 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 proposed GraphLoRA method effectively transfers well-trained GNNs to diverse graph domains, addressing the challenges of transferability in GNNs. This is achieved by introducing a Structure-aware Maximum Mean Discrepancy (SMMD) to align node feature distributions across source and target graphs. Additionally, low-rank adaptation injects a small trainable GNN alongside the pre-trained one, bridging structural distribution gaps while mitigating catastrophic forgetting. A structure-aware regularization objective enhances adaptability with scarce supervision labels. Extensive experiments on eight real-world datasets demonstrate GraphLoRA’s effectiveness against fourteen baselines, tuning only 20% of parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs Neural Networks (GNNs) are good at handling some graph tasks, but they have trouble moving from one type of graph to another. This is a problem because GNNs can’t easily learn from graphs that look different. To fix this, researchers came up with GraphLoRA, a way to make well-trained GNNs work better on new types of graphs. They use two main ideas: aligning the features between old and new graphs, and adding some extra learning to help the GNN adapt. This works surprisingly well – even when only a small part of the GNN is changed. |
Keywords
» Artificial intelligence » Gnn » Low rank adaptation » Regularization » Transferability
