## 2 Literature Review
At present, there are three main research directions in the online community discovery: (1) Based on user attributes and interaction relations, the hidden links between users are discovered, and the clustering is transformed into graph partitioning problem; (2) When mining the text of a user's Microblog content or comments, user clustering based on characteristics of interest is realized; (3) Considering a Microblog user's contact and text content, content-based user interest community discovery and interaction-based user contact community discovery are realized separately, and then the two communities are integrated to form a user community with interest and network structure. For example, [PERSON] described the Microblog user relationship in the form of a network which depicts user behavior and influence in the network according to changes in network structural attributes [20]. [PERSON] proposed a user association mining algorithm based on spectral clustering [21]. [PERSON] proposed the similarity of Microblog users based on common follows and common followers [22]. This paper integrates user structured data and unstructured data to construct eigenvectors in vector space to achieve user clustering structure in the network. There are mainly two kinds of graphs--modular structure graphs and random intersection graphs. In the modular graphs such as social network, biological modular, and physical systems, the points in the networks are not distributed randomly. The problem of community detection is proposed against this background to deal with the modular structure of some networks. Subspace clustering is a new type of clustering algorithm for high-dimensional data. The idea is that high-dimensional data belongs to a low-dimensional subspace in essence [23]. According to the different subspace search strategies, it can be divided into top-down search strategies and bottom-up search strategies. Subspace clustering algorithms are mainly divided into five types: matrix decomposition-based methods, iterative methods, algebraic methods, statistical methods, and spectral clustering-based methods [24]. The first four kinds of methods must set the number of subspaces and their dimensions in advance, and they are sensitive to the outline points in the data set. The spectral clustering method transforms the clustering problem into the optimal graph partition problem by using the relationship between data points, without knowing the number and dimension of subspaces [25; 26]. SSC is a subspace spectral clustering algorithm based on compressed sensing theory. It uses sparse representation theory to represent data as a linear combination of other data in the same subspace, constructs the similarity matrix through the sparse coefficient matrix, and clusters data with a spectral clustering algorithm [27; 28; 29]. The SSC optimization models can be solved by different approaches such as the alternating direction method of multipliers (ADMM), orthogonal matching pursuit (OMP), etc. [24; 27]. In recent years, scholars have proposed different optimization strategies and wider applications of SSC algorithms. In this paper, SSC is introduced into an online social network community discovery, which expands the research field of this method. The research on the clustering of short text users in mobile social networks has received extensive attention, Main problems that need to be solved in short text research are high- dimensional features and sparsity of short text. In order to deal with this problem, this paper proposes a new method of social network member community discovery based on SSC, which collects users' structured data (including user attributes and user behaviors) and unstructured data (user comments) to construct a vector space model and establish the similarity of feature words. The matrix projection method is then applied to the high-dimensional data to reduce the dimension. Then, a new community discovery model for short text based on SSC is proposed.