Open Large-Scale Online Social Network Dyn

Online social networks have quickly become the most popular destination on the World Wide Web. These networks are still a fairly new form of online human interaction and have gained wide popularity only recently within the past three to four years. Few models or descriptions of the dynamics of these systems exist. This is largely due to the difficulty in gaining access to the data from these networks which is often viewed as very valuable. In these networks, members maintain list of friends with which they share content with by first uploading it to the social network service provider. The content is then distributed to members by the service provider who generates a feed for each member containing the content shared by all of the member's friends aggregated together. Direct access to dynamic linkage data for these large networks is especially difficult without a special relationship with the service provider. This makes it difficult for researchers to explore and better understand how humans interface with these systems. This dissertation examines an event driven sampling approach to acquire both dynamics link event data and blog content from the site known as LiveJournal. LiveJournal is one of the oldest online social networking sites whose features are very similar to sites such as Facebook and Myspace yet smaller in scale as to be practical for a research setting. The event driven sampling methodology and analysis of the resulting network model provide insights for other researchers interested in acquiring social network dynamics from LiveJournal or insight into what might be expected if an event driven sampling approach was applied to other online social networks. A detailed analysis of both the static structure and network dynamics of the resulting network model was performed. The analysis helped motivated work on a model of link prediction using both topological and content-based metrics. The relationship between topological and content-based metrics was explored. Factored into the link prediction analysis is the open nature of the social network data where new members are constantly joining and current members are leaving. The data used for the analysis spanned approximately two years.