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Link and sign prediction in complex networks bring great help todecision-making and recommender systems, such as in predicting potentialrelationships or relative status levels. Many previous studies focused ondesigning the special algorithms to perform either link prediction or signprediction. In this work, we propose an effective model integration algorithmconsisting of network embedding, network feature engineering, and an integratedclassifier, which can perform the link and sign prediction in the sameframework. Network embedding can accurately represent the characteristics oftopological structures and cooperate with the powerful network featureengineering and integrated classifier can achieve better prediction.Experiments on several datasets show that the proposed model can achievestate-of-the-art or competitive performance for both link and sign predictionin spite of its generality. Interestingly, we find that using only very lownetwork embedding dimension can generate high prediction performance, which cansignificantly reduce the computational overhead during training and prediction.This study offers a powerful methodology for multi-task prediction in complexnetworks.

Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks