Let's start with some relatively simple examples, complex only in having more than a single filter. In this case, we will create one filter and then use a second condition as a subfilter to the first. Here are the steps:

We should now see the following result in your graph window:

Nodes filtered by classname equals 3B and gender equals F

We now have a graph that has been quickly reduced to just 11 nodes of the original 236 by merging two simple filters. From here, we can easily change the classname value or the gender value that allow us to filter the network in a rapid fashion. This filter could also be saved for later use by right-clicking on and selecting Save, which will park the filter in the Saved queries folder. Let's move to another example; this time we will cover an example that combines three separate filters into a single query by using the subfilter capability.

This time we'll really narrow our graph down by merging three filters as follows:

Here's what our filter should look like:

Complex filter that combines gender and degree range excluding the classnames 1A and 1B

We now have a filter that identifies all highly connected female members of the network who are not in classnames 1A or 1B. Here's our result:

Results of filtering on female, degree range of 60-98, and not in 1A or 1B

You can see how quickly we whittled the network to just 12 nodes by combining these three conditions. From here it's also easy to tweak the settings—perhaps we would like to view only the 10 most connected female students. Simply increasing the degree range threshold will enable us to see these results refreshed with the new conditions. In fact, if we increase this value from 60 to 62, we are left with just nine students who meet all three criteria.

What would happen if we hadn't nested these criteria in a single query, but had chosen to isolate each filter? You can probably guess what the results will look like. Here are the three filter conditions we just presented, but now they act as standalone queries:

Individual filters not nested in a single query

To create this query, simply follow the same steps we previously used, with one exception. Rather than nesting each filter inside another as a subfilter, this time add each one as its own query, as shown in the preceding screenshot. Run them one at a time by clicking on the Select and Filter buttons.

First, we'll run the Partition filter with the value set to F, which results in the following graph:

Filtering on partition equal to F

As you might have anticipated, the network has been reduced to roughly half by hiding all nodes with gender equal to male.

Next, we'll filter on Degree Range, to identify all network members ranging between 60 and 98 degrees. Make sure the lower value is set to 60, and then filter the results to see the following output:

Filtering on degree range between 60 and 98

Now you are seeing only those nodes with a degree level of 60 or greater, including both male and female members. The prior filter is effectively overwritten by the new condition, rather than combining them as we previously saw. To verify that this is the case, go to Data Laboratory and view the results to confirm the presence of both male and female students.

Let's apply our third filter—the NOT (Nodes) operator that excludes classnames 1A and 1B. You should see the following results:

NOT (Nodes) with partition on classname equal to 1A and 1B

Not only do we have both genders visible in our dataset, but also students from the entire degree range from 18 through 98. The only missing members are those from classnames 1A and 1B, which can be confirmed by viewing the Data Laboratory results.

So while these individual filters can obviously not be used in the same manner as the nested versions, they do have a considerable value. If you wish to filter your network across many conditions then simply set those up in the Queries window, and then you have the ability to toggle through an array of filters to learn more about your network. Think of it in the same way as a statistician might work with single variable crosstabs to learn more about a dataset before moving on to a higher level of complexity.

Now let's tackle our remaining examples, starting with an instance where we will focus on the ego network of an individual student. To complete this example, we'll work with the INTERSECTION filter, found in the Operator folder within the Filters tab, as follows:

Your finished graph will resemble the following graph, with the remaining nodes filtered out of the process, and all first degree connections displayed like this:

Completed ego network with masked edges

Here's a screenshot for how your complete query will appear:

Intersection of masked edges and ego network

Remember to set the second (redundant) Equal (Id) value to make sure that the filter operates as expected. This might seem slightly quirky at first, but once learned, it becomes simple to apply to many other examples.

Now that we have run through a fairly complex example using the intersection logic, we'll end the chapter with an instance using the UNION operator. If you think of intersections as being parallel to the use of AND constructs in database queries, then unions are much closer to the OR condition. One notable difference is the requirement that union queries to get based on a single data attribute, where intersections derive their power from merging conditions across multiple attributes.

Given that this example is easier to follow than the last, we'll first introduce the filter conditions of the filter that we are creating, and then view the results. Here's how we want our filter to appear, which can be achieved by performing the following steps:

Union query with low and high degree ranges

To create the filter, follow these steps:

Your result should look like this:

Results of union query on degree ranges

We now have a graph that shows the least connected members of the network, as seen near the perimeter of the graph, and the most highly connected nodes concentrated in the center. The results can be verified in the data laboratory, where we see no nodes with degree values greater than 33 or less than 70. We could follow a similar process using other data attributes such as classname.

I'm sure that you have also thought of some additional applications for complex filters using your own datasets, and it is my hope that these examples will provide both a springboard and reference for some of these processes.