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

TL;DR

This paper introduces a unified model integration algorithm that combines network embedding, feature engineering, and classification to improve link and sign prediction accuracy in complex networks, achieving state-of-the-art results.

Contribution

It presents a novel integrated framework for simultaneous link and sign prediction, emphasizing low-dimensional network embedding for efficiency.

Findings

Achieves state-of-the-art or competitive performance on multiple datasets.

Low-dimensional embeddings can still produce high prediction accuracy.

The unified approach simplifies multi-task prediction in complex networks.

Abstract

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.