Summary of Can Self Supervision Rejuvenate Similarity-based Link Prediction?, by Chenhan Zhang et al.
Can Self Supervision Rejuvenate Similarity-Based Link Prediction?
by Chenhan Zhang, Weiqi Wang, Zhiyi Tian, James Jianqiao Yu, Mohamed Ali Kaafar, An Liu, Shui Yu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposes a novel method called Self-Supervised Similarity-based Link Prediction (3SLP) that integrates self-supervised graph learning techniques into similarity-based link prediction. The goal is to develop more informative node representations, replacing traditional node attributes as inputs in the similarity-based LP backbone. This approach is suitable for unsupervised scenarios where there are no known link labels. The method introduces a dual-view contrastive node representation learning (DCNRL) with crafted data augmentation and node representation learning. Experimental results on benchmark datasets demonstrate a significant improvement of 3SLP, outperforming the baseline by up to 21.2% in terms of AUC. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to predict links between nodes in a graph is developed without using labeled data. This method, called Self-Supervised Similarity-based Link Prediction (3SLP), makes node representations more informative and useful for predicting links. The approach works by learning from the structure of the graph itself, rather than relying on pre-existing information about the nodes. This is important because often we don’t have labeled data, but we still want to make predictions. The method does better than existing approaches in this area, with a significant improvement of up to 21.2%. |
Keywords
» Artificial intelligence » Auc » Data augmentation » Representation learning » Self supervised » Unsupervised
