Modeling Multi-user Behaviour in Social Networks

Social networks, and the behaviour of groups of online users, are popular topics in modeling and classifying Internet traffic data. There is a need to analyze online network performance metrics through suitable workload benchmarks. We address this issue with a Multi-dimensional Hidden Markov Model (MultiHMM) to act as a Multi-User workload classifier. The MultiHMM is an adaptation of the original HMM, using clustering methods and multiple trace-training for the Baum-Welch algorithm. The goals of the MultiHMM are to classify multiple online user streams with minimal processing needs, represent burstiness and correlation among groups of users and to improve security measures in the social network. Experiments are carried out using multiple traces from Twitter data, where original traces are analysed and compared with the MultiHMM-generated traces. The metrics involved in validating our model include means, standard deviations, skew ness and autocorrelation, and we discuss applications and extensions of our model.