Modeling Multi-user Behaviour in Social Networks

Abstract

Social networks, and the behaviour of groups of online users, are popular topics in modeling and classifiyng 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 BaumWelch 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 MultiHMMgenerated traces. The metrics involved in validating our model include means, standard deviations, skewness and autocorrelation, and we discuss applications and extensions of our model.

Higher response time moments for M/M/1 discriminatory processor sharing queues

“…Alternatively, multiple streams of TCP packets can be modeled as job arrival processes using variations of the Markov-modulated Poisson process (MMPP), which is a special case of MAP. Discretized MMPPs (or hidden Markov models) replicate the burstiness of TCP packet traces, which can be clustered in groups, and, hence, allow model parameters to converge on multiple traces simultaneously at reduced computational complexity [10]. Further, arrival parameters of queueing models can be updated incrementally via online EM learning algorithms [2,6,14], which are suitable for live systems.…”