Its parameters are defined in 4 different panels which are largely similar to the options proposed in the network mapping script. However, no network is plotted. Instead, a visualisation of the contingency matrix is produced.

## Contingency Matrix visualisation

## Nodes Selection

#### First Field

Define the first field from which the nodes will be selected. It will populate the x-axis.

#### Second Field

Different from the first field! Define the second field from which the nodes will be selected. It will populate the y-axis.

#### Number of nodes

Number of top nodes to consider in each field (max=100).

## Contingency matrix options

#### Contingency Analysis Measure

Choose the contingency analysis measure to use. Three measures are available to highlight the discrepancy between both distributions. First a matrix of expected values of co-occurrences is computed following the null hypothesis that distribution are independent. Either classic oriented *Chi2 measure* or *deviation measure* are proposed. **Chi2 measure** directly indexes the color of the cell to its chi2 score (that is the ratio between the square of the number of co-occurrences between A(i) and B(j) minus its expected number under null hypothesis divided by this same expected number). The **deviation measure** maps the increase of observed co-occurrences of A(i) and B(j) compared to the expected value. If a cell has value 6 for instance it means that the number of joint mentions is 600% higher than expected. If negative, -4 for instance, it means that the number of expected co-occurrences is 400% higher than the observed number of co-occurrences. Cramer measure is also an option.

#### Evaluate wether deviations are statistically significant

“Evaluate whether deviations are statistically significant” will perform a Fisher exact test on each cell value to detect whether the measured deviation has a **p-value above 0.05**, in which case an arrow (“x”) indicating that the signal is spurious will be added in the middle of the cell.

#### Automatic block reordering of contingency matrix

Automatic block reordering of contingency matrix (may be long when the number of nodes exceeds 10). Automatic block re-ordering simply reorders the entries in each field such that adjacent columns and rows are similarly result in a matrix which is easier to read.

#### Contingency Analysis logscale

One can also activate the colormap logscale option (recommended) so the smaller deviations are not faded by larger ones.

#### Manually assign a maximum value

By default the colormap will extend to the maximum deiviation of each time step. Setting a custom limit will stabilize colormaps along time periods.