Published Paper Details:

Social networking services is a web based platform which is used by the people to evolve social networks or social relationships with each other who share similar profession or individual interests, activities, events, ideas or real life connections. The popularity of SNS is emerging rapidly and has become a huge date source to analyze social networks. The widespread content by users is very important in social networking services. In this, I proposed a method for finding information diffusion models for the data in social networking sites which can be obtained by using process mining techniques .For this, first we filer the information by expelling inconsistent users. Further, to decrease the complexity nature of SNS filtered information a user-centric clustering approach accomplished utilizing a novel measure called user intimacy value that quantifies the degree of relationship between users ,depending on modularity maximization strategy is carried out. This clustered information is converted to an event log which is utilized as input for process discovery algorithm Infoflow miner. The Infoflow miner utilizes response weight, that can be characterized as, the degree of impact or effect that one user activities have on other user activities. Final outcome of the technique is a graph which represents the flow of information among user networks present in the original information.