Managing social networks: Applying the percolation theory methodology to understand individuals' attitudes and moods

This conceptual study aims to search for ways in which to describe and manage the states of groups of individuals by researching percolation processes in social networks, and identifying percolation thresholds at which negative moods and undesirable ideas will be freely distributed within the network. The methodology proposed for describing the state and dynamics of individuals' moods implements percolation models. Percolation processes are modelled using specially designed software. Within the chosen model framework, percolation theory generates answers to the following questions: a) how does society become clustered into groups of individuals united by certain views according to the average number of connections per node? and b) what proportion of negatively-tuned individuals can bring the network into such a state wherein harmful information can be transmitted between two randomly chosen individuals? Focus is placed on discussing practical implications of the results in order to both predict people's behaviours, and to manage groups of people in networks. This work may be of interest to scientists, specialists in consumer behaviour, sociologists and politicians.