## 2 Literature Review  
This paper researches the user community discovery method for online social networks based on SSC. Subsequently, three relative aspects are summarized from text representation, user interest community research, and subspace clustering theory. The primary problem of text clustering is to transform the unstructured data into information that can be processed by computational method. At present, the vector space model is usually used in the text representation. Document sets are represented as vector sets in space, and the similarity of document semantics is transformed into the similarity of space vectors [7]. The semantics of the words in the document constitutes the meaning of the whole document. Each word is a feature item, and the importance of each word is reflected by the weight of the featured item. Boolean function, frequency function, and term frequency-inverse document frequency (TF-IDF) function are commonly used to calculate the weight of feature words. The TF-IDF function is commonly used because of its accuracy and simplicity. [PERSON] and [PERSON] proposed a short text similarity measure based on Web semantic kernel function, which calculated the similarity between short texts by adding semantic information of text features into the kernel function [10]. [PERSON] extended [PERSON]'s Web semantic kernel function to obtain Web-related similarity measures [11]. [PERSON] calculated the weight of features in each text and extracted keywords to represent the text, but ignored the feature words with lower frequency in the concise text [12]. [PERSON] improved the weight of traditional TF-IDF function and added a synonym to solve the sparse problem of short text [13]. [PERSON] and other scholars proposed a user-based LDA (Latent Dirichlet Allocation) topic model to classify Microblog short texts [14]. With the development of mobile networks, researchers focus more on clustering analysis of short texts. [PERSON] transformed the short text clustering problem into frequent item set clustering and retrieved short text based on subject phrases to achieve massive short text clustering more accurately [5]. The accuracy of the existing clustering algorithm is usually low in the high-dimensional sparse short text. To deal with this problem, [PERSON] constructed the unweighted graph of the short text set and the document similarity matrix was obtained [15]. It can bring about storage and computation difficulty if the spectral clustering algorithm is used directly in large data sets [6,16,17]. Therefore, a new method for online social network user community detection is proposed based on compressed sensing theory and SSC in this paper. Users with high degrees of relevance in online social networks usually aggregate to constitute a group [18]. Microblog is a real-time medium, and user interests mainly stem from users interested in friends and acquaintances. The connection between any two people does not mean the interaction between them, and the link structure of the social network cannot reveal the actual interaction between people. So it is necessary to find the hidden social network in the Microblog network. [PERSON] built a Microblog user interest model of user attributes, keywords, and user behaviors [3]. [PERSON] personalized user recommendations and achieved good results from the users' personal basic information, released content, tags, and the number of followers as features reflecting user interests [19].