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Community detection is one of the most important problems in social network analysis in the context of the structure of the underlying graphs. Many researchers have proposed their own methods for discovering dense regions in social networks. Such methods are only designed with links of the underlying social network. However, with the development of recent applications, rich edge content can be available...
Currently, the detection of global community structure in networks has gathered a lot of attention. Most of the methods need global knowledge of the graphs which would be unrealistic to get when the graphs are too large or evolve too quickly. Moreover, sometimes we are only interested in the community structures of some given nodes, not all nodes. So detecting the community of a given node i.e. local...
Along with informationization advancement thorough and Internet rapid development, there exists millions of websites on the Internet. Search engines become a mediator to connect web users and websites. The query logs in which recorded daily contains a wealth of knowledge about the actions of the users of search engines, and as such they contain valuable information about the interests, the preferences,...
Community detection and tracking in social network is an important research area for many applications which are widely applied in complex systems. Recently there has been a surge of investigation in this area, fueled largely by interest in social networks, but also by interest in bibliographic citations and telecommunication records. However, due to the computational cost of the traditional algorithm...
An interesting property of network is that the information is not only contained in the entities, but also in the links between them. As the structure of the co-authorship network can greatly influence its function and reflect how the internal information is exchanged. We attempt to get deep insight of the features in a co-authorship network at a university. This is done by the following two steps...
As acquiring and retaining the most profitable customers are challenging tasks of service providers, various CRM tools are used to support these processes. Traditional CRM methods focus on various customer profitability models in different scenarios based on their past profit contribution. Social network analysis provides a natural way to understand the relationships between customers; however, this...
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