Predicting Spread Probability of Learning-Effect Computer Virus

Table 2

Notations.

G (V, E)	A scale-free network with sets of nodes V and arcs E
Nnode	The number of nodes
ei, jei,j ∈ E	Such that information can be transmitted directly from node i to j
Deg (i)	Degree of node i
V (i)	Subset of nodes that receive information from node i
ti	Infected timeslot of node i ∈ V
pi,j	Spread probability that the computer virus is spread out from an infected node i ∈ V to a susceptible node j ∈ V (i)
pi,j,t	Temporal learning-effect spread probability of the computer virus from nodes i ∈ V to j ∈ V (i) for any valid timeslot t
S (t)	Proportions of susceptible nodes
I (t)	Proportions of infectious nodes
R (t)	Proportions of recovered nodes
β	Transmission rates
γ	Recovery rates
PR	Probability that users clicking on nodes randomly will arrive at i
PR (i)	The i^th element in PR for all i ∈ V
d	A damping factor between 0 and 1
M	Normalized adjacency square matrix
Ma,b	Element in the a^th row and the b^th column in M
I	Identity matrix
X	State vector
X (i)	Value in the i^th coordinate of vector X
T	Timeslot vector
Ti,t	The t-lag temporal vector of node i
Y	Temporal vector
TARGET	The first infected node
Nt	The number of temporal state vector candidates for time lag t
nt	The number of feasible temporal vectors for time lag t