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Conventional online social networks (OSNs) are implemented in a centralized manner. Although centralization is a convenient way for implementing OSNs, it has several well known drawbacks. Chief among them are the risks they pose to the security and privacy of the information maintained by the OSN; and the loss of control over the information contributed by individual members.
Community detection is an important research issue in complex network mining. In this paper, firstly, we define central nodes, called Extended Local Max-Degree (ELMD) nodes in a complex network. All the central nodes are used for the community expanding. We also prove that ELMD method is more precise and dispersed than local max-degree method in the real datasets. Secondly, we propose an improved...
Community detection is important for many complex network applications. A major challenge lies in that the number of communities in a given social network is usually unknown. This paper presents a new community detection algorithm-Distance Centrality based Community Detection (DCCD). The proposed method is capable of detecting the community of network without a preset community number. The method...
Most existing community detection methods ignored the dynamic nature, a key property of social networks and these methods often lead to unreasonable divisions when faced with dynamic environments. Although there have been several dynamic community detection algorithms, low accuracy and low performing are still two challenging problems to be solved. In order to solve above problems, we proposed a new...
Since the emergence of BLOG, it not only represents a new network technology, but also means the beginning of a new life style. How to utilize and mine the BLOG content which contains hidden sentiment and real-time update is a big challenge in the data-mining domain. As most of the existing method for network text's topic mining is achieved through clustering text's topic and label which are labeled...
Community detection in complex networks is a topic of considerable recent interest within the scientific community. For dealing with the problem that genetic algorithm are hardly applied to community detection, we propose a genetic algorithm with ensemble learning (GAEL) for detecting community structure in complex networks. GAEL replaces its traditional crossover operator with a multi-individual...
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