Formal Modeling and Analysis of User Activity Sequence in Online Social Networks: A Stochastic Petri Net-Based Approach

The continuous interaction of users and information aggregation has become a social phenomena over massive social media platforms. However, the uncertainty of users’ behavior is leading great challenges to social networks analysis in terms of system structure, evolution characteristics, dynamic behavior, and so forth. Thus, this article proposes a formal user behavior modeling and analysis approach. First, aiming at identifying the behavior patterns of user activity sequence, we present a user activity transition system model based on stochastic Petri net (SPN), which can formally depict the process and structures of social users click activities. Then, the average number of tokens in each place, the probability density function of the tokens, the token flow rate of transitions, and the time spent in each state are analyzed by isomorphic it into a Markov chain (MC), respectively. These four indicators are used to evaluate the performance of the proposed system model. The experimental results demonstrate that the proposed approach can help us to understand the rules of users’ first activity and activity preferences, so as to provide practical suggestions for the development of social networking platforms and content recommendation.