For the
remaining three clusters in the network graph, the density of the
subnetworks makes it difficult to detect all the merchants with high
degree using direct observation. You can use a statistical graph and
the local selection feature to filter the data in the network graph.
The following
figure shows the result of using a scatter plot with local selection
mode to parse the visualized data. In this graph, the blue nodes that
are visible represent all the merchants with high degree in groups
2, 3, and 4.
Merchants with High Degree in Groups 2, 3, and 4
The following
steps describe how to create this graph in the ccFraudData.nvw project:
-
Click
the node data table to activate it.
-
Create
a scatter plot using the
GraphsScatter Plot menu option. Select
TOTAL_TRANS_NUM as the X variable and
GROUP as the Y variable.
-
In the
scatter plot, select the merchants with high degree in groups 2, 3,
and 4. To do this, select all blue nodes with an X coordinate greater
than or equal to 3 and a Y coordinate greater than or equal to 2.
Hold the CTRL key to select multiple nodes.
The following
figure shows the scatter plot with the merchants selected:
Scatter Plot with Some Merchants Selected
-
Select
DataSelection ModeLocal. An icon is displayed
in the upper left corner of each graph. The icon for the network graph
(
) indicates that the graph has an observer-union role.
-
In the
scatter plot, select all customers (all red nodes) with a Y coordinate
greater than or equal to 2. The customer nodes appear in the network
graph.
In the
network graph, the blue nodes that are visible represent all the merchants
in groups 2, 3, and 4 that have high degree.
-
Select
Tools Interactive Zoom. Then click the network graph to zoom in on the graph.
-
If you
want to see the names of the merchants, select
ToolsLabel, and then click the blue nodes in the network graph.
In summary,
this example shows how to use
SAS/GRAPH Network Visualization Workshop
to investigate credit card fraud. You used the visualization features
and observation filtering capabilities to identify merchants that
warrant additional scrutiny with regard to fraudulent credit card
transactions.