The Dection And Application Of Hierarchical Community In Complex Network

Community detection is an important task for mining the structure and function of networks. It enables us to view a complex network at macro-levels, by grouping its nodes into units whose characteristics and interrelationship are easier to analyze and understand. The detection of community is considered as an important characteristic after Power-Law, Small-World, and High Clustering Coefficient. But it has its unavoidable drawbacks:The real network is not so-called flat module structure. The community structure shows a significant level feature; mostly the small communities with high density are nested in large communities with low density. Thus, the small communities together form large communities and larger organizations can be merged into the larger society. Hierarchical structure of network is efficient. Specific modules perform specific functions. Based on the community detection, the introduction of hierarchical community detection will make the community structure be richer and more efficient to reveal the structural characteristics of the network itself.In the paper, we solve out the problem how to efficiently mine the meaningful community structure in complex network and what hierarchical features underlying these community structures. The main contributions of this dissertation include the following:firstly, we have proposed the community detected algorithm called BSCHE which is based on structure connecting. According to the definition of shared neighbors, the algorithm uses a global similarity threshold to find links whose connectivity density is greater than the threshold, and it can also identify the hubs and outliers of the network, looking for the potential different level of abstraction of the community. After we implemented the algorithm, we constructed the system based on interactive visual analysis. The system is based on MVC framework; in the visual process we use Prefuse as it is one of the best visualization related technologies. In data source layer we provide different data access. In the visualization layer we provide numbers of visual rendering and layout strategies. The interactive based visual analysis platform provides visual display for different data set and data analysis and results analysis.