Next Article in Journal
A Novel Neural Network-Based Method for Medical Text Classification
Previous Article in Journal
Reinforcement Learning Based Query Routing Approach for P2P Systems
Open AccessArticle

Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach

by Zihe Zhou 1 and Bo Tian 2,*
1
College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(12), 254; https://doi.org/10.3390/fi11120254
Received: 23 October 2019 / Revised: 22 November 2019 / Accepted: 5 December 2019 / Published: 9 December 2019
The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets. View Full-Text
Keywords: sparse subspace clustering; community detection; microblog text analysis; online social network sparse subspace clustering; community detection; microblog text analysis; online social network
Show Figures

Figure 1

MDPI and ACS Style

Zhou, Z.; Tian, B. Research on Community Detection of Online Social Network Members Based on the Sparse Subspace Clustering Approach. Future Internet 2019, 11, 254.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop