Profiling of Social Network Users using Proximity Measures

The era of today's world is based on multiple online social network such as twitter, Facebook, LinkedIn and many more. User's use this online social network for commination in terms of text, audio, video, images, gif's and so on which leads to enormous amount of unstructured data generation. Hence, it becomes inevitable to analyze these unstructured data into meaningful knowledge which can be applied to various applications such as link prediction, criminology, public health, recommendation system and many more. Most applications of social networks require user profile data to analyze the data. In this paper, we propose a graph based methodology to connect user profiles based on their attributes similarity and build a social network graph of connected users. The methodology is tested on LinkedIn data set and results are promising. The methodology addresses various issues associated with unstructured data analysis.