SocialNetworkAnalysis

Social media such as Facebook, Twitter, LinkedIn, and Instagram led us to realize the importance of social network analysis. Social network analysis has its applications in social media networks, friendship (trust) network, disease transmission, and sexual relationships. In business, social network analysis is applied to fraud detection, viral marketing, terrorism and crime prevention, promising technology identification, and key player identification. For example, world leading companies seek for key scientific leaders and factors using social network analysis. The number of its applications is getting larger. Social network analysis is a special case of network analysis. The first step for network analysis is to define what objects you want to deal with and what relationships between the objects you want to focus on. How you define the objects and the relationships between them will decide the definition of your network. An object becomes a node in a network. The centrality of the node is determined by its relations to the other nodes. Analyzing different types of centrality will give you an idea on the position of the node among the others. The weight of an edge between two nodes represents the strength of their relation. The communities clustered by a machine learning algorithm may give you some hint to the abstractions and the notions of the whole network. This course will help students think a system including a social system, knowledge system, and communication system in relational and structural way that may lead the students to come up with insights. Bibliometrics (i.e., scholarly communication) and informetrics will be covered. Besides, technology and market will be examined by network analysis. Sample codes for network analysis will be given to the students in Java, Python, and R. Sample dataset for exercising the codes will be also given.