Pattern Mining in Large Social Networks: from Network Structure to User Behavior

• Main Authors: Jing-Kai Lou, 羅經凱
• Other Authors: Chin-Laung Lei
• Format: Others
• Language: en_US
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/7rme6f

博士 === 國立臺灣大學 === 電機工程學研究所 === 102 === Social media has prospered in the last decade, which grants people a hitherto wide range to deliver their views, and to develop their social circles through the Internet. The worldwide popular social media services such as Facebook, facilitate people to manifest themselves, to engage the connections, and to join communities. Rise of social media unleashes people from the geographic limitation, and makes the world more connected. The users globally form an online society via the social media services. Thank to the trackable online activities, the interactions on social media services can be completely saved, which leaves us a hitherto abundant resource to study social behaviors of humans. The tremendous and various data in social media greatly draws attention to the analysis of the social network. A social network refers to a network structure consisting of the actors on social media. Research interests on social network analysis are widely diverse. Generally speaking, one primary study is the social network structuralism that postulates the network structure implicitly reflects the human behavior. The interests would be narrow down to the relevance between the structural patterns and social issues. On the other hand, another research line particularly devotes to the content or the attributes of the social actors. We endeavoured to explore the issues on social media in both aspects. We aim at the social online gaming as our main target to study for two reasons. First, online gaming is considered as a killer application since the emergence of World Wide Web, and has been part of the primary reasons people use the Internet. Second, the realistic nature of the interactions in online gaming opens up the possibility of viewing the world inside an MMORPG as a laboratory for observing individual and social behaviors of humans in as fine details as the game design would allow. The work is divided into two parts. In the first part, we investigate the cause of the unique social network structure. The findings from the observations inspire us a number of practical applications. In the second part, we shift our focus to the attributes of the human behavior in online gaming which functions as a virtual society. We exploit the observations to predict the duration of an online game, and also to confirm the sociological and psychological theories. The contributions of the dissertation are briefly listed as follows. We perform a comprehensive observation of the individual activities and social interactions in a number of long-lived online games. With the knowledge gained from the investigation, we develop the following applications for academic or industrial purposes. 1) We propose an approach to fast estimate individual centrality according to the dynamics of a social network. 2) We design a preference diffusion model with theoretical guarantees to manage high dimensional information. We assess the quality of this model by proving its convergence and several other important properties. 3) We propose a quantitative way for the addictiveness index of a game according to the gamers'' online sessions. Then, we develop a forecasting model for the addictiveness index of a game not released in the market yet. 4) we investigate the phenomenon of ``gender swapping,'' which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online during social interactions when playing as in-game avatars of their own real gender or genderswapped, and discuss the effect of gender role and self-image in virtual social situations.