Research On Complex Networks & Personalized Information Service On Internet

After the discovery of small-world phenomena and scale-free characteristics of complex networks in the late 20^th century, the research of complex networks has been gotten more and more attention. Researchers from different fields studied complex networks from every level. Complex networks has become a vital topic crossed with many other research fields. Nowadays the research of complex networks has been mainly focused on the two aspects: One aspect is theoretical analysis and simulation, in which new theoretical models and methods were proposed continually; The other aspect is applied research, in which new structures and phenomena of real-world network were discovered continually.With the development of information technology and the popularity of Internet, Web2.0 has become an important trend in the application of Internet. There are many non-linear, self-organize, and emergence phenomena in Web2.0 systems. So it is important that the theoretical methods of complex networks are applied to Web2.0, which can not only benefit understanding Web2.0 and guide the further development of Web2.0, but also can accelerate the theoretical research of complex networks.Personalization is a major feature of Web2.0. Personalized information service has been one of the hottest research points in the applied research of Internet. User profiling, clustering, classification and automatic recommendation are the crucial techniques in personalized information service. So the research of these crucial techniques will promote the large-scale personalized information service on Internet efficiently.This dissertation focuses on the above aspects and combines the theoretical methods of complex networks with the research of personalized information service. The research work of this dissertation can be summarized as follows:Firstly, this dissertation combines the research of complex networks with Web2.0. Main works are summarized as follows: (1) The non-linear mechanism, self-organize and emergence phenomena in Web2.0 systems are studied using the theoretical methods of complex networks. (2) A novel algorithm for finding the overlapping community in the complex networks with intersection structure is proposed. (3) The overlapping community structure in the complex networks with intersection structure is analyzed statistically.Secondly, the features of complex networks are applied to the automatic keywords extraction and clustering analysis. Detailed works are summarized as follows:(l) The small-world structure in human language network of Chinese is studied. A novel automatic keywords extracting algorithm based on the features of complex networks is proposed. This algorithm extracts those words with higher degree and clustering coefficient in the language network as keywords. Its goal is to extract keywords which may be relatively low frequency, but do great contribute to the subject of the text. (2) The definitions of the weighted complex networks features are given after the deeply studying on the features of complex networks. A novel...