  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.
