On the proximity for community detection

• Main Authors: Yeh, Ying-Ling, 葉盈伶
• Other Authors: Shiu, Shang-Ying
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/27788511939498279561

碩士 === 國立臺北大學 === 統計學系 === 101 === Social network analysis (SNA) provides a systematic method to uncover social network structures. One of the main components of SNA is the adjacency matrix, which carries information about the relationship between pairs of vertices in a network. This thesis work presents an approach that can help to strengthen this information. By simple operations on the adjacency matrix, proximity matrices are created. Each proximity matrix quantifies the relationship between pairs of vertices from a different point of view. With the use of the proximity matrices, additional information may be retrieved. To detect community structures from the proximity matrices, the elliptical seriation algorithm (Chen, 2002) implemented in GAP (generalized association plots, Chen, 2002) is considered, which is a java-designed exploratory data analysis (EDA) software. The performance of GAP is compared to that of the Girvan-Newman algorithm (Girvan and Newman, 2002). Community structures detected by the two algorithms are evaluated and examined.