重庆邮电大学学报（自然科学版）2020，Vol.32Issue(5)：759-768,10.DOI:10.3979/j.issn.1673-825X.2020.05.008
基于集成学习的复杂网络链路预测及其形成机制分析
Link prediction and analysis of formation mechanism of complex networks based on ensemble learning
摘要
Abstract
To predict new or missing connections between a node and other existing nodes in the network, link ( edge) pre-diction has sparked increasing research interest in recent years. Recently, a variety of algorithms with different characteris-tics have been proposed to solve the problems of link prediction, for which each algorithm only takes into account a kind of information of the network and thus leads to a one-sided result. We present an ensemble learning method to combine all the single algorithms and take comprehensive account of the most information. An experiment succeeds on eight real networks, in which we extract 17 different features using local topological indexes, global topological indexes and recommended algo-rithm. The results suggest that AUC of ensemble learning are 2％ to 17％ higher than the best single algorithms and the highest score can be achieved 0.9624. Furthermore, we analyze the structure and formation mechanism of different types of networks according to the degree distribution and feature selection from random forest. We obtain some significant insights a-mong formation mechanism, network types and features. The features conducted from certain mechanisms or assumptions, are really reflecting the driven force of connection of node pairs, and therefore can be suitably used for link prediction.关键词
集成学习/链路预测/复杂网络/形成机制Key words
ensemble learning/link prediction/complex networks/formation mechanism分类
信息技术与安全科学引用本文复制引用
张淼,梁延研,黄相杰..基于集成学习的复杂网络链路预测及其形成机制分析[J].重庆邮电大学学报（自然科学版）,2020,32(5):759-768,10.基金项目
The Science and Technology Development Fund of Macau(0025/2018/A1,0019/2018/ASC,0008/2019/A1,0010/2019/AFJ,0025/2019/AKP) （0025/2018/A1,0019/2018/ASC,0008/2019/A1,0010/2019/AFJ,0025/2019/AKP）
