• 人工智能 •

### 基于密集连接卷积神经网络的链路预测模型

1. 中南民族大学 计算机科学学院, 武汉 430074
• 收稿日期:2018-11-15 修回日期:2019-01-18 出版日期:2019-06-10 发布日期:2019-06-17
• 通讯作者: 吴淋涛
• 作者简介:王文涛(1967-),男,河北邯郸人,副教授,博士,主要研究方向:计算机网络与控制;吴淋涛(1994-),男,湖南茶陵人,硕士研究生,主要研究方向:计算机网络、数据挖掘;黄烨(1993-),男,湖北大悟人,硕士研究生,主要研究方向:知识表示、神经网络;朱容波(1978-),男,湖北潜江人,教授,博士,主要研究方向:移动计算、无线网络协议设计与性能优化。
• 基金资助:
国家自然科学基金资助项目（61772562）；中南民族大学中央高校基本科研业务费专项基金资助项目（CZY18014）；中南民族大学研究生学术创新基金后期资助项目（3212018hqzz029）。

### Link prediction model based on densely connected convolutional network

1. College of Computer Science, South-Central University for Nationalities, Wuhan Hubei 430074, China
• Received:2018-11-15 Revised:2019-01-18 Online:2019-06-10 Published:2019-06-17
• Supported by:
This work is partially supported by the National Natural Science Foundation of China (61772562), the Fundamental Research Funds for the Central Universities of South-Central University for Nationalities (CZY18014), the Academic Innovation Research Fund for Graduates of South-Central University for Nationalities (3212018hqzz029).

Abstract: The current link prediction algorithms based on network representation learning mainly construct feature vectors by capturing the neighborhood topology information of network nodes for link prediction. However, those algorithms usually only focus on learning information from the single neighborhood topology of network nodes, while ignore the researches on similarity between multiple nodes in link structure. Aiming at these problems, a new Link Prediction model based on Densely connected convolutional Network (DenseNet-LP) was proposed. Firstly, the node representation vectors were generated by the network representation learning algorithm called node2vec, and the structure information of the network nodes was mapped into three dimensional feature information by these vectors. Then, DenseNet was used to to capture the features of link structure and establish a two-category classification model to realize link prediction. The experimental results on four public datasets show that, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) value of the prediction result of the proposed model is increased by up to 18 percentage points compared to the result of network representation learning algorithm.