Description

Recently, stochastic graph in which weights associated with edges are random variables is suggested as a better candidate as a graph model for real-world network applications with time-varying nature for social network analysis. By choosing stochastic graph as a graph model of a social network, it is called stochastic social network. In this book, we first introduce several re-definitions of network measures for stochastic social networks and then we introduce some intelligent algorithms for computation of network measures for social network analysis under the situation that the weights associated with the edges of the network are random variables with unknown probability distribution functions. Intelligent algorithms can guide the process of sampling the edges of the network in order to provide good estimates for the probability distribution functions of the edges of the network.