Dynamical Analysis of an SE2IR Information Propagation Model in Social Networks

Algorithm 1

: IPC SE2IR algorithm.

	Input Given a network .
	Output The information propagation states at the final time.
(1)	Calculate the relative influence weight .
(2)	Give different social reinforcement intensity, perceived value of users, subjective preference coefficient of users, and information timeliness at some time : , , , and .
(3)	Compute the parameters at the time :
	3.1 ifthen
	else
	3.2 ifthen
	else,
	3.3 ifthen,
	else,
	3.4
(4)	Substituting all parameters into system (1), obtain the information propagation states on the network :
	4.1. Confirm the globally asymptotically stable spread-free equilibrium point and local spread equilibrium point.
	4.2. Analyze the information propagation state on the network when the Mindegree node, Meandegree node, and Maxdegree node are used as initial nodes.
	4.3. Analyze the impact of parameter on the process of information propagation.
(5)	Changing the perceived value, social reinforcement intensity, and information timeliness, obtain the information propagation states on the network :
	5.1. Obtain the proportions of infected nods and removed nodes by choosing the perceived values.
	5.2. Obtain the proportion of infected nods by choosing the social reinforcement intensity and its intervention time.
	5.3. Obtain the proportion of infected nodes by choosing the information timeliness of users.
(6)	Output the different information propagation states at different times and under different values of the parameters.