Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

Support vector machine (SVM) is a kind of machinelearning method based on statistical learning theory, which has become the hotspot of machine learning research. In the paper we discuss the application research of the SVM in communication such as multi-user detector in 3 rd CDMA technology, image processing and speech identification. In the conclusion section, we elicit the advantage and the shortcoming of the SVM and looks forward to its attractive application research prospect.

This paper present a neural network system -GTNN for identifying the gapping of main journal bearing of engines. Calculation results are compared with the experiment data, and them error of them is acceptable. Finally the explanation of the calculation result was given.