Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.
Structure ontology characteristic of blogosphere is one of the hot topics in social computing. It has an important research value for blog dissemination, blog community discovery, blog mining etc. From the empirical research on a number of blogospheres in the real world, we find that the blogosphere is characterized by local centralized large-scale complex social network, whose characteristic is closely related to blog channel, and composed of multiple discrete small social networks. The previous methods by using structure reductionism or functional reductionism cannot fully explain the evolution and development of the blogosphere. Based on the structure ontological viewpoint, This paper presents the blogosphere is composed of belief space, group space and content space. The content space is a information network, the group space is a network of relationships, the belief space is a cultural network, among of which, each one has endogenous rules of evolution, and also interacts with each other, under the common actions of the downward causal relationship and the upward causal relationship, it shows a wealth of structural features.
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