期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 8, 期 3, 页码 2613-2624出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2021.3100889
关键词
Predictive models; Complex networks; Prediction algorithms; Task analysis; Social networking (online); Indexes; Bridges; link prediction; sign prediction; complex network; deep learning
资金
- Zhejiang Provincial Natural Science Foundation of China [LR18A050001, LR18A050004]
- Natural Science Foundation of China [61873080, 61673151]
- Major Project of The National Social Science Fund of China [19ZDA324]
The study introduces an effective model integration algorithm for link and sign prediction in complex networks. Experiment results show that the proposed model achieves state-of-the-art or competitive performance for both link and sign prediction, and using low-dimensional network embedding can also generate high prediction performance.
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.
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