Experimental Study of Evolving Communities in Online Social Networks

In the past few decades, the advancement in technology and the internet has leveraged the application of social networks in the world. Millions of people connect through social networks irrespective of their geographical boundaries. These users tend to form communities on common ground, such as similar hobbies, school, work, and much more. Deep level mining and analysis of these communities, connections within these communities, and the connections within the users of these communities divulge abundant data about the underlying features of these complex networks. This thesis focuses on detecting communities at specific times to track the change in memberships. Doing so would allow one to observe how these communities evolved over the given period of time. For evaluation, four temporal social network datasets were considered from different backgrounds, such as a college network database, Facebook database, and two question-answer-comment website databases. Three different kinds of community detection methodologies such as Leiden, Louvain and Infomap are used to detect communities among these datasets. Two types of analysis are done on community data, structural level, and temporal level. The temporal level analysis is further conducted on the community level (both pairwise and individual) and node level. Specific metrics used in these kinds of analysis differ from each other in various ways. The resulting outcomes of this project may be used for predicting the memberships that may form eventually. Moreover, similar patterns may assist data analytics experts in detecting influential members, malicious activities in the network, a population of individuals suffering from specific health issues, and designing the recommendation systems