Framework for Faction of Data in Social Network Using Link Based Mining Process

Recent online social networks such as Twitter, Facebook, and LinkedIn have hurriedly grown in reputation. The resulting accessibility of a social network data supplies an unparalleled occasion for data analysis and mining researchers to resolve useful and semantic information in a broad range of fields such as social sciences, marketing, management, and security. Still, unprocessed social network data are enormous, noisy, scattered, and susceptible in nature, in which some challenges is faced when applying data mining tools and analyzing tasks in storage, efficiency, accuracy, etc. In addition to that there are many problems related to the data collection and data conversion steps in social network data preparation. We focused on the endeavor for privacy preserving social network conversion which provides method for better protection and identification of privacy for social network users and to maintain the convenience of social network data.