Summary of Tensor-fused Multi-view Graph Contrastive Learning, by Yujia Wu et al.
Tensor-Fused Multi-View Graph Contrastive Learning
by Yujia Wu, Junyi Mo, Elynn Chen, Yuzhou Chen
First submitted to arxiv on: 20 Oct 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 In a machine learning paper, researchers propose Tensor-Fused Multi-View Graph Contrastive Learning (TensorMV-GCL), a novel approach to enhance graph neural networks’ ability to learn rich representations from unlabeled graph-structured data. The method integrates extended persistent homology with GCL representations and facilitates multi-scale feature extraction, reducing computational overhead through tensor aggregation and compression. Experiments on molecular, bioinformatic, and social network datasets demonstrate TensorMV-GCL’s superiority, outperforming 15 state-of-the-art methods in graph classification tasks across 9 out of 11 benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph contrastive learning has emerged as a promising approach to enhance graph neural networks’ ability to learn rich representations from unlabeled data. A new framework called Tensor-Fused Multi-View Graph Contrastive Learning (TensorMV-GCL) is proposed, which integrates extended persistent homology with GCL representations and facilitates multi-scale feature extraction. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Machine learning
