Dynamic social network analysis and performance evaluation

Social media in today's age is on a tremendous rise in terms of its usage and the enormous amount of data it generates which includes personal details of users, their images and the content that is being shared on various open source platforms. This has led to a lot of research and analysis of such networks and data that exists in social media. This paper is focused on dynamic analysis of social networks, where snapshots of network are taken at fixed intervals and are analysed on various performance measures. The real time e-mail dataset of a company (Enron) has been evaluated and visualised dynamically. The network measures are evaluated at each timestamp and clustering is performed on that data and its performance is calculated on various measures. Tabu search optimisation algorithm has been used for clustering the timestamped data and a comparison is done between the fixed size cluster and variable size clusters. The results suggests that for certain time stamps the value of precision, recall and f measure for fixed size clusters are better than the variable size clusters. These measures can further be used for the selection of the dynamic clustering techniques for social network analysis.