Predicting the dynamics of social circles in ego networks using pattern analysis and GA K‐means clustering

The tremendous amount of content generated on online social networks has led to a radical paradigm shift in the interest of people to group friends dynamically and share content selectively. At large, social networking sites (e.g. Google+, Facebook, Twitter, etc.) offer users with various controls over categorizing their family members, friends, colleagues, etc. into one or more ‘circles’ that they want to share content with. However, it is typically impossible to design social circles in large networks and update their number and size, whenever networks grow. Aiming at predicting the dynamics of formation and evolution of social circles, we performed several experiments on ground‐truth data, and found that studying patterns of network and profile features at individual level rather than studying circle as a whole can greatly enhance the understanding of social circles development in online social networks. In this review, we first present a comprehensive study of the structural behavior of circles, and then build an observation that within every circle there exist some key elements, termed as ‘ Node of Creations ( NoCs ) ’, which play an important role in the development, survival, and evolvability of circle structures. We, therefore, propose a Genetic Algorithm–based framework to determine these key elements ( NoCs ) and differentiate Ego networks into non‐overlapping, hierarchically nested as well as overlapping circles by leveraging knowledge from the identified patterns in order to assist K‐means clustering. We have performed our experiments using Facebook and Twitter datasets and the experimental results clearly demonstrate the effectiveness of our scheme. WIREs Data Mining Knowl Discov 2015, 5:113–141. doi: 10.1002/widm.1150 This article is categorized under: Technologies > Machine Learning Technologies > Prediction Technologies > Structure Discovery and Clustering