In this work we study virtual social networks known as Facebook. It is used by\nmillions of people worldwide, gathering a combination of virtual elements\nand real world components. We suggest a probabilistic model to describe the\nlong-term behavior of Facebook. This model includes different friendship\nconnection between profiles, directly or by suggestion. Due to web�s high interactivity\nlevel, we simplify the model assuming Markovian dynamic. After\nthe model is established we propose Complete Transversality (CT) communication\nconcept. CT describes people interaction that reflects profile behaviour\nand leads to estimators that measure this interaction. Then we introduce a\nweakness version of CT named Segmental Transversality (ST). Within this\nframework we develop estimators that allow hypothesis testing of CT and ST.\nAnd then, in ST context we propose performance measures to address a priori\nsegmentation�s quality.
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