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Community is formed by individuals such that those within a group interact with each other more frequently than with those outside the group. Community mining or detection involves discovering groups in a network where an individual's group memberships are not explicitly given. Much of the research on detecting communities in co-authorship networks has been done using publicly available datasets....
We want to build our students to face the world challenges. Our duty is to reshape our earlier plans according to current era. Our Main Focus of this paper is to provide the awareness about education for students with the help of social networks and discussed various learning tools available online to enhance the effectiveness of their learning and knowledge sharing habits. No single e-learning method...
Peer influence in social networks has been studied for over four decades by social scientists and marketing researchers. Due to recent growth of internet technologies and online social networks, research on peer influence has gained even more attention over the last decade. But, most extant empirical work measuring peer effect faces challenges due to selection problem, difficulty in separating source...
Social network is the group of individuals who have common interest. Data mining has the greatest attention of research over the past decade. The machine learning field evolved from the broad field of artificial intelligence, in which the machine (computers) can be enabled to think or act as intelligent as humans. Classification is the machine learning problem in which the given dataset can be classified...
Many systems in sciences, engineering and nature can be modeled as networks. Examples include the Internet, WWW and social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted...
There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers. In this paper, we propose a new measure...
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