In today's society, social networking has been integrated into everyone's life. The detection of network community has been a hotspot in recent years, and it has been widely used in fraud prevention, personalized recommendation, risk control and other fields. In this paper, we present an improved community detection algorithm for fusing topology and attribute information (FTAI) based on Non-Negative Matrix Factorization (NMF). Firstly, we use the attribute similarity matrix instead of a binary matrix based on string matching to cope with the sparsity of the attribute matrix. Next, we introduce the transfer matrix and the attribute feedback adjustment method to fuse the topological matrix and the attribute matrix. Then, we deduce the iteration formula of each matrix with a rigorous mathematical method, which shows the reliability of the algorithm. Finally, we evaluate our method with extensive experiments by using the data set from a real academic social network (SCHOLAT). Experimental results show that FTAI is superior to other baseline algorithms. The results also indicate that FTAI is more flexible, robust and suitable for community detection in social networks with complex attribute information.

Compared with traditional course websites, the open online course platforms have a larger number of courses, and course recommendation is becoming increasingly important. In this paper, we shall propose a course recommendation model based on academic social networks, a hybrid method combing with association rules algorithm and an improved multi-similarity algorithm of multi-source information, which can recommend courses according to potential relationships between courses and users implicit interests. The proposed model is applied to SCHOLAT. Judging from our experimental results, the new model is capable of reducing cold-start problem and providing better accuracy.