Link Prediction Algorithm Based on Word2vec and Particle Swarm

被引：0

作者：

Jia C.-F. ^{[1
]}

Han H. ^{[1
]}

Lv Y.-N. ^{[1
]}

Zhang L. ^{[2
]}

机构：

[1] School of Science, Wuhan University of Technology, Wuhan

[2] Wuhan Antiy Technology Co., Ltd, Wuhan

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 08期

关键词：

Deep learning; Feature extraction; Imbalance problems; Link prediction; Particle swarm optimization;

D O I：

10.16383/j.aas.c180187

中图分类号：

学科分类号：

摘要：

There are two major problems in the link prediction: The difficulty of feature extraction and the imbalance of class data. In this paper, an algorithm based on word vector is proposed by using the deep learning feature extraction algorithm in text processing and the particle swarm optimization algorithm in the optimization problem. The method firstly generates a set of node sequences through random walks, and uses the Word2vec algorithm to extract node sequence features. Then, under the supervised conditions, the particle swarm algorithm was used to filter the extracted features, and the resampling parameters were determined to solve the imbalance problem of category data. It also analyzes the computational complexity of different link prediction algorithms. Finally, the algorithm of this paper is compared with three link prediction algorithms based on similarity, deep learning, and unbalanced data, and empirically studied in four different time series networks. The results show that the link prediction algorithm proposed in this paper has more accurate prediction accuracy and is more stable and universal. Copyright © 2020 Acta Automatica Sinica. All rights reserved.

引用

页码：1703 / 1713

页数：10

共 28 条

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