基于特征分类的链路预测方法综述

doi:10.11896/jsjkx.190700136

Abstract

Abstract: As an important research direction of network science research, link prediction of complex network has been more and more attention from experts from various disciplines, and it can make use of existing network information, such as the features of the nodes and edges, to predict possible future relationships, missing information in the network, and new or disappearing information, identify the false interactions, evaluate network evolution mechanism and conduct network reconfiguration, etc.Current articles on link prediction mainly come from the expert in engineering, computer science and physics.They are independent and lack cooperation, and there are few review articles combining multidisciplinary link prediction.The goal of this article is from the perspective of computer science and physics comprehensive to review, analyze, and discusse the research progress of link prediction algorithm based on feature classification, introduces a variety of feature extraction techniques in this field.This paper firstly introduces the idea of layering into the classification of link prediction algorithm, which divides the classification model into three layers, the metadata layer, features classification layer and feature extraction layer.The classification model includes two parts and seven aspects, that is, the prediction algorithm is divided into two parts, features extraction method and features learning method.The seven aspects are the similarity based methods, probabilistic methods, likelihood based methods, matrix factorization based method, random walk based method, neural network based method and custom loss function based method.This classification method covers many classical and latest link prediction techniques in various disciplines, including GNN, GCN, RNN and RL, which are the most popular link prediction techniques in graph neural networks.The differences of these algorithms in model complexity and prediction performance are studied, and the challenges of current link prediction technology in the future are discussed.

Key words: Complex network, Feature classification, Graph neural network, Link prediction, Machine learning

CLC Number:

TP391

WANG Hui, LE Zi-chun, GONG Xuan, WU Yu-kun, ZUO Hao. Review of Link Prediction Methods Based on Feature Classification[J].Computer Science, 2020, 47(8): 302-312.

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