Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909368884133888 |
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| author | Liu, Haoxin |
| author_facet | Liu, Haoxin |
| contents | Link prediction is a fundamental task in graph analysis. Despite the success of various graph-based machine learning models for link prediction, there lacks a general understanding of different models. In this paper, we propose a unified framework for link prediction that covers matrix factorization and representative network embedding and graph neural network methods. Our preliminary methodological and empirical analyses further reveal several key design factors based on our unified framework. We believe our results could deepen our understanding and inspire novel designs for link prediction methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21325 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction Liu, Haoxin Machine Learning Artificial Intelligence Link prediction is a fundamental task in graph analysis. Despite the success of various graph-based machine learning models for link prediction, there lacks a general understanding of different models. In this paper, we propose a unified framework for link prediction that covers matrix factorization and representative network embedding and graph neural network methods. Our preliminary methodological and empirical analyses further reveal several key design factors based on our unified framework. We believe our results could deepen our understanding and inspire novel designs for link prediction methods. |
| title | Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.21325 |