Line graph contrastive learning for link prediction. (August 2023)
- Record Type:
- Journal Article
- Title:
- Line graph contrastive learning for link prediction. (August 2023)
- Main Title:
- Line graph contrastive learning for link prediction
- Authors:
- Zhang, Zehua
Sun, Shilin
Ma, Guixiang
Zhong, Caiming - Abstract:
- Highlights: We design a novel contrastive learning framework based on line graph to be suitable for link prediction on sparse and dense graphs. We propose a cross-scale contrastive learning strategy to maximize the mutual information between subgraph and line graph. The dual perspectives contrastive progress to some extent avoids the problem of inconsistent prediction on the similarity based methods with a single view. Our comprehensive experiments on six datasets from diverse areas demonstrate that our model has better performance on generalization and robustness than the SOTA methods. Abstract: Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes. However, the similarity-based approaches have some challenges in information loss on nodes and generalization ability on similarity indexes. To address the above issues, we propose a Line Graph Contrastive Learning (LGCL) method to obtain rich information with multiple perspectives. LGCL obtains a subgraph view by h -hop subgraph sampling with target node pairs. After transforming the sampled subgraph into a line graph, the link prediction task is converted into a node classification task, which graph convolution progress can learn edge embeddings from graphs more effectively. Then we design a novel cross-scale contrastive learning framework on the line graph and the subgraph toHighlights: We design a novel contrastive learning framework based on line graph to be suitable for link prediction on sparse and dense graphs. We propose a cross-scale contrastive learning strategy to maximize the mutual information between subgraph and line graph. The dual perspectives contrastive progress to some extent avoids the problem of inconsistent prediction on the similarity based methods with a single view. Our comprehensive experiments on six datasets from diverse areas demonstrate that our model has better performance on generalization and robustness than the SOTA methods. Abstract: Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes. However, the similarity-based approaches have some challenges in information loss on nodes and generalization ability on similarity indexes. To address the above issues, we propose a Line Graph Contrastive Learning (LGCL) method to obtain rich information with multiple perspectives. LGCL obtains a subgraph view by h -hop subgraph sampling with target node pairs. After transforming the sampled subgraph into a line graph, the link prediction task is converted into a node classification task, which graph convolution progress can learn edge embeddings from graphs more effectively. Then we design a novel cross-scale contrastive learning framework on the line graph and the subgraph to maximize the mutual information of them, so that fuses the structure and feature information. The experimental results demonstrate that the proposed LGCL outperforms the state-of-the-art methods and has better performance on generalization and robustness. … (more)
- Is Part Of:
- Pattern recognition. Volume 140(2023)
- Journal:
- Pattern recognition
- Issue:
- Volume 140(2023)
- Issue Display:
- Volume 140, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 140
- Issue:
- 2023
- Issue Sort Value:
- 2023-0140-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-08
- Subjects:
- Line graph -- Contrastive learning -- Link prediction -- Node classification -- Mutual information
Pattern perception -- Periodicals
Perception des structures -- Périodiques
Patroonherkenning
006.4 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00313203 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.patcog.2023.109537 ↗
- Languages:
- English
- ISSNs:
- 0031-3203
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 27019.xml