Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization

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

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.

Multiple Partial Regularized Nonnegative Matrix Factorization for Predicting Ontological Functions of lncRNAs

Long non-coding RNAs (LncRNA) are critical regulators for biological processes, which are highly related to complex diseases. Even though the next generation sequence technology facilitates the discovery of a great number of lncRNAs, the knowledge about the functions of lncRNAs is limited. Thus, it is promising to predict the functions of lncRNAs, which shed light on revealing the mechanisms of complex diseases. The current algorithms predict the functions of lncRNA by using the features of protein-coding genes. Generally speaking, these algorithms fuse heterogeneous genomic data to construct lncRNA-gene associations via a linear combination, which cannot fully characterize the function-lncRNA relations. To overcome this issue, we present an nonnegative matrix factorization algorithm with multiple partial regularization (aka MPrNMF) to predict the functions of lncRNAs without fusing the heterogeneous genomic data. In details, for each type of genomic data, we construct the lncRNA-gene associations, resulting in multiple associations. The proposed method integrates separately them via regularization strategy, rather than fuse them into a single type of associations. The results demonstrate that the proposed algorithm outperforms state-of-the-art methods based network-analysis. The model and algorithm provide an effective way to explore the functions of lncRNAs.