| Summary: | 碩士 === 國立中央大學 === 資訊管理學系 === 102 === Quick development of the Internet and huge explosion of the social network make people rely highly on the social network software in their daily life. Most researches on community detection in the past refer to K-means, agglomerative, graph or Girvan- Newman algorithm. The interest of this study has been directed to the algorithm of agglomerative. One possible deficiency of this method is that it always ignores the nodes which are on the edge of the community. Therefore, in the merging step, the nodes on the edge could be allocated to the wrong community. This study is aimed to improve the performance of the algorithm by finding the core node and the local community as new indexes for agglomerate. In the experiments, the results are compared with (Lim &; Datta, 2013; Qiong &; Ting-Ting, 2010; Tiantian &; Bin, 2012) to show the effectiveness of the method developed in this study.
|
|---|
