一种改进复杂网络链接和符号预测的有效模型集成算法
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks
摘要：
复杂网络中的链接和符号预测为决策和推荐系统带来了很大的帮助，例如预测潜在的关系或相对状态级别。许多先前的研究集中在设计特殊的算法来执行链接预测或符号预测。在这项工作中，我们提出了一个有效的模型集成算法，包括网络嵌入，网络特征工程，和一个集成的分类器，它可以在同一个框架中执行的链接和符号预测。网络嵌入能够准确地表示拓扑结构的特征，配合强大的网络特征工程和集成分类器可以实现更好的预测。在多个数据集上的实验表明，尽管该模型具有通用性，但它在链接和符号预测方面都能达到最先进或有竞争力的性能。有趣的是，我们发现仅使用非常低的网络嵌入维数可以产生高的预测性能，这可以显着降低训练和预测过程中的计算开销。该研究为复杂网络中的多任务预测提供了一种强有力的方法。
英文摘要：
Link and sign prediction in complex networks bring great help to decision-making and recommender systems, such as in predicting potential relationships or relative status levels. Many previous studies focused on designing the special algorithms to perform either link prediction or sign prediction. In this work, we propose an effective model integration algorithm consisting of network embedding, network feature engineering, and an integrated classifier, which can perform the link and sign prediction in the same framework. Network embedding can accurately represent the characteristics of topological structures and cooperate with the powerful network feature engineering and integrated classifier can achieve better prediction. Experiments on several datasets show that the proposed model can achieve state-of-the-art or competitive performance for both link and sign prediction in spite of its generality. Interestingly, we find that using only very low network embedding dimension can generate high prediction performance, which can significantly reduce the computational overhead during training and prediction. This study offers a powerful methodology for multi-task prediction in complex networks.
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。
