统一矩阵分解与图嵌入方法，提升链接预测效果
Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction
- 提出统一框架，整合矩阵分解与图神经网络
- 实证发现关键设计因素影响预测性能
- 适合图学习与推荐系统研究者参考
链接预测是图分析中的基础任务。尽管各类图机器学习模型在该任务上取得成功，但对不同模型的统一理解仍显不足。本文提出一个统一的链接预测框架，涵盖矩阵分解、代表性网络嵌入及图神经网络方法。通过初步的方法论与实证分析，揭示了基于该框架的若干关键设计因素。结果有望深化对链接预测机制的理解，并启发新方法的设计。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。