用于链接预测的图特征学习与提取研究综述
Review on Learning and Extracting Graph Features for Link Prediction
摘要：
复杂网络中的链接预测由于其在生物网络、社交网络、交通网络、电信网络以及最近的知识图谱中的广泛应用而引起了跨学科研究界的极大关注。许多研究利用链接预测方法，以stto找到丢失的链接或预测未来链接的可能性，以及用于重建网络，推荐系统，隐私控制等。这项工作提出了一个广泛的审查国家的最先进的方法和算法提出了这个问题，并将它们分为四个主要类别：基于相似性的方法、概率方法、关系模型和基于学习的方法。此外，本文还提供了一组网络数据集，可用于研究链接预测。最后，我们讨论了最近的发展和未来的研究方向。
英文摘要：
Link prediction in complex networks has attracted considerable attention from interdisciplinary research communities, due to its ubiquitous applications in biological networks, social networks, transportation networks, telecommunication networks, and, recently, knowledge graphs. Numerous studies utilized link prediction approaches in order sto find missing links or predict the likelihood of future links as well as employed for reconstruction networks, recommender systems, privacy control, etc. This work presents an extensive review of state-of-art methods and algorithms proposed on this subject and categorizes them into four main categories: similarity-based methods, probabilistic methods, relational models, and learning-based methods. Additionally, a collection of network data sets has been presented in this paper, which can be used in order to study link prediction. We conclude this study with a discussion of recent developments and future research directions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
