set of pairs of nodes. A diagram is a 2 dimensional expression of a graph, which is the drawing of the web of the links interconnecting at the nodes. In a map, faces are relevant, which are determined by the links and their intersections. A network refers to either a graph, or a diagram. The map is not used **in this** paper. The nodes in the graph represent persons, and the links represent the transmission of influence. The links between the nodes which appeared in the intelligence data set is inferred with the maximum likelihood estimation (MLE). A parametric form is defined to describe the link topology. The probability where the influence transmits from an initiating node nj **to a responder node** nk is rjk**. The influence transmits to multiple responders independently in parallel.**
It is similar to the degree of collaboration probability in trust modeling [14].

Eq.(5) gives constraints on rjk.

$$0\leq r_{j k}\leq1,\ r_{j j}=1\ (0\leq j,k\leq|n|-1).$$
$\left(5\right)$. 
The quantity fj is the probability where the node nj **becomes an initiator.**
The parameters rjk and fj are denoted by r **collectively. It is the target to**
estimate from the observation data set.

$$\mathbf{r}=\{r_{j k}\}\cup\{f_{j}\}\ (0\leq i,j\leq|n|-1).$$
r = {rjk} ∪ {fj} (0 ≤ **i, j** ≤ |n| − 1). (6)
The logarithmic likelihood function [5] is defined by eq.(7). In statistics, a likelihood function is a conditional probability function of the observation given the parameters of a statistical model. It plays a key role in statistical inference such as Bayes' Law. The probability where D occurs for given r **is denoted by** p(D|r).

L(r) = log(p(D|r)). (7)
The individual observations are assumed to be independent. Eq.(7) **becomes**
eq.(8).

$$L(\mathbf{r})=\log(\prod_{i=0}^{|d|-1}p(d_{i}|\mathbf{r}))=\sum_{i=0}^{|d|-1}\log(p(d_{i}|\mathbf{r})).$$
$$({\mathfrak{h}})$$
$$({\boldsymbol{\delta}})$$
$$\left({\mathfrak{g}}\right)$$

The quantity fk|ij **in eq.(9) is the probability where the presence or absence**
of the node nk as a responder to the stimulating node nj **coincides with the** observation di.

$$f_{k|i j}=\left\{\begin{array}{l l}{{r_{j k}}}&{{\quad\mathrm{if~}d_{i k}=1\mathrm{~for~given~}i\mathrm{~and~}j}}\\ {{1-r_{j k}}}&{{\quad\mathrm{otherwise}}}\end{array}\right.\,.$$

Eq.(9) is equivalent to eq.(10) since the value of dik **is either 0 or 1.**

$$f_{k|i j}=d_{i k}r_{j k}+(1-d_{i k})(1-r_{j k}).$$
fk|ij = dikrjk + (1 − dik)(1 − rjk). **(10)**
The probability p(D|r**) in eq.(8) is expressed by eq.(11).**

$$(10)$$
$\left(11\right)$. 