| Summary: | 碩士 === 國立中央大學 === 資訊管理學系 === 107 === Social network analysis has attracted researchers’ attention due to its widespread practicability. Several techniques are developed for extracting useful knowledge from users’ regularities. Opinion leader discovery is one essential task which has great commercial and political values. By identifying the opinion leaders, companies or governments could manipulate the selling or guiding public opinion, respectively. Additionally, detecting the influential comments is able to understand the source and trend of public opinion formation. However, prior studies mainly focus on finding opinion leader in a static social network. Actually, in real applications, social networks are usually evolved with time; few research efforts have been elaborated on finding opinion leaders in dynamic social network. In this study, a novel algorithm, DOLM, is proposed to efficiently find the opinion leaders from a dynamic social network. We utilize a network emerging method to construct a dynamic social network, and then detect the community structure to tackle the information overlapping problem. Then, DOLM develops a clustering-based leadership analysis to find out the opinion leader in a dynamic social network. The experimental study shows that the proposed algorithm could effectively capture the characteristic of a dynamic social network and solve the information overlapping problem. We also apply DOLM on several real datasets to show the efficiency and scalability for opinion leader discovery.
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