Researches On Complex Network Clustering Analysis And Its Application

Complex networks has become the Internet, sociology, biology and other fields of a basic task. Aggregation node is a feature in many of complex networks, known as clusters or groups. The aggregation of complex networks can reveal many potential problems and also reflect the relationship between network nodes. Previous studies found that non-weighted aggregation of complex networks. This thesis will examine the complex network that is the weighted complex network aggregation semantics.First, this thesis studies the weighted complex network cluster. This complex networks converts various feedbacks into the semantic complex network. The thesis proposes an agglomerative hierarchical clustering method based on semantic core, named CACNSC, to analyze the semantic accumulation under different granularities.Second, the thesis shows a mechanism to recommend a few important multimedia objects for authority annotation and states the automatically annotation method using semantic information of limited multimedia objects. The proposed method is verified by the ideal feedbacks with high noise and the real feedbacks of Princeton Shape Benchmark. The method performs quite well not only in semantic clustering but also in annotation.Finally, this thesis implements a three-dimensional model retrieval system. With the increase in user feedback, the semantics of nodes will be more abundant. This can make more precise semantics to improve the retrieval efficiency.