The Research Of Community Feature Extraction And Feature Prediction In Complex Network

Recent studies on complex network provide theoretical model and research method for researchers to understanding real-word networks. Understanding the community feature is helpful to understand the network topology and group characteristics better, while feature prediction is a necessary step to ensure the correctness of feature extraction. Therefore, feature extraction and prediction in complex network is a challenging and prospective area. At the same time, the continued exponential growth in both the volume and the complexity of information is giving birth to a new challenge to the researchers. With respect to this challenge, multiple parallel computing platforms, such as MapReduce and BSP, has been emerging.Research in this paper are based on the massive telecom data, we present a comprehensive multidimensional study of telecom group feature from topology, gender, age three aspects and provide parallel algorithms for this feature extraction. After compare several relational classifiers, we use the communicaton characteristics of mobile phone users to increase the precison greatly and provide parallel algorithm for feature prediction. The tasks are as follows.Based on current research, after introducing main content and research result of different sorts of group characteristics, the community detection methods are provided, and the system of the network community features is proposed, consist of modularity, distribution of node degree, clustering coefficient and average shortest path. We propose parallel algorithms for all the community features mentioned above, using MapReduce or BSP parallel computing model according to different conditions.In this paper, we present a node-centric Network learning framework in which classifiers comprise a local classifier, a relational classifier, and a collective inference procedure. After introduce four relational_classifiers three collective inference algorithms,that relaxation labeling is suitable for parallelization and the MapReduce parallel algorithm is presented.We study the communication behaviors based on the topology of telecom network and attributes of mobile phone users, including gender, age, calling and short message informations to find the hidden behavior patterns of the daily interaction of human beings. We find that people tend to communicate more with each other when they have high similarity. The telecom service provider can target customers and percise marketing based on this analysis.We choose weighted-voted relational neighbor classifier (WVRN), with highest predicton precison, to predict features in telecom network. Besides the topology information, we also use the communication features of mobile phone users in the relational model. We combine the WVRN with a communication decision tree, achieving93.17%precison in gender prediction and90.13%precison in age prediction.