计算机应用 ›› 2019, Vol. 39 ›› Issue (5): 1411-1415.DOI: 10.11772/j.issn.1001-9081.2018112559

• 网络与通信 • 上一篇    下一篇

基于隐空间映射的带符号网络上的顶点分类

盛俊1,2, 顾沈胜1, 陈崚1   

  1. 1. 扬州大学 信息工程学院, 江苏 扬州 225000;
    2. 扬州市职业大学 信息工程学院, 江苏 扬州 225000
  • 收稿日期:2018-12-04 修回日期:2018-12-14 出版日期:2019-05-10 发布日期:2019-05-14
  • 通讯作者: 盛俊
  • 作者简介:盛俊(1972-),男,江苏扬州人,副教授,硕士,CCF会员,主要研究方向:数据挖掘、复杂网络分析;顾沈胜(1992-),女,江苏扬州人,硕士研究生,CCF会员,主要研究方向:推荐系统、复杂网络分析;陈崚(1951-),男,江苏扬州人,教授,博士生导师,CCF会员,主要研究方向:数据挖掘、并行与分布式处理、复杂网络分析。
  • 基金资助:
    国家自然科学基金资助项目(61379066,61472344,61402395);江苏省自然科学基金资助项目(BK20140492);江苏省教育厅自然科学基金资助项目(13KJB520026);江苏省六大人才高峰项目(2011-DZXX-032);2018年度扬州市职业大学校级科研项目(2018ZR04)。

Node classification in signed networks based on latent space projection

SHENG Jun1,2, GU Shensheng1, CHEN Ling1   

  1. 1. School of Information Engineering, Yangzhou University, Yangzhou Jiangsu 225000, China;
    2. School of Information Engineering, Yangzhou Polytechnic College, Yangzhou Jiangsu 225000, China
  • Received:2018-12-04 Revised:2018-12-14 Online:2019-05-10 Published:2019-05-14
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61379066, 61472344, 61402395), the Natural Science Foundation of Jiangsu Province (BK20140492), the Natural Science fund of Jiangsu Provincial Department of Education (13KJB520026), the Six Talent Peaks Project in Jiangsu Province (2011-DZXX-032), the College-level Scientific Research Project of Yangzhou Polytechnic College in 2018 (2018ZR04).

摘要: 社会网络顶点分类在解决实际问题中有广泛的应用,但绝大多数现有的网络顶点分类算法都集中在无符号的网络,而在边上具有符号的社交网络上的顶点分类算法却很少,且负链接对于符号网络分析的作用大于正链接。研究了符号网络中顶点的分类问题。首先将正、负网络映射到相对应的隐空间,提出基于隐空间的正负链接的数学模型;然后提出优化该模型的迭代算法,通过对隐空间矩阵和映射矩阵的迭代优化,来对网络中的顶点进行分类。由带符号的社会网络数据集的实验结果证明,该算法在数据集Epinions上得到结果的F1值在11以上,在数据集Slashdo上得到结果的F1值在23.8以上,与随机算法相比具有较高的精确度。

关键词: 带符号网络, 隐空间, 映射, 顶点分类

Abstract: Social network node classification is widely used in solving practical problems. Most of the existing network node classification algorithms focus on unsigned social networks,while node classification algorithms on social networks with symbols on edges are rare. Based on the fact that the negative links contribute more on signed network analysis than the positive links. The classification of nodes on signed networks was studied. Firstly, positive and negative networks were projected to the corresponding latent spaces, and a mathematical model was proposed based on positive and negative links in the latent spaces. Then, an iterative algorithm was proposed to optimize the model, and the iterative optimization of latent space matrix and projection matrix was used to classify the nodes in the network. The experimental results on the dataset of the signed social network show that the F1 value of the classification results by the proposed algorithm is higher than 11 on Epinions dataset, and that is higher than 23.8 on Slashdo dataset,which indicate that the proposed algorithm has higher accuracy than random algorithm.

Key words: signed network, latent space, projection, node classification

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