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What's New?
VisuaLinks 3.0 to be released in February!
After a lengthy beta test cycle, VisuaLinks 3.0 is finally ready to ship.
We will be releasing VisuaLinks 3.0 by the end of February - so get your updates soon!
This version of VisuaLinks, as you've probably noticed in our new feature coverage the last few issues, sports a number of improvements. There are many new features, as well as changes to the user interface to make VisuaLinks even easier to use.
Keep an eye on your e-mail for notification of the official release of VisuaLinks 3.0. It's just days away.
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View Filter
One of the new features in VisuaLinks is the ability to interactively filter the contents of the View. The View Filter feature lets you hide objects and associations using attribute values. As values are selected and de-selected in the Filter Panel, the objects in the corresponding View are filtered.
The View Filter offers a number of capabilities. You can:
Hide objects and associations
Mark objects and associations
Beacon marked objects
Hide unconnected objects
The Filter Panel lists each of the data types (objects and associations) in the current View. Objects are listed first, alphabetically, followed by associations, also listed alphabetically.
There is a section for each object and association in the View. Each section displays the attributes for the data type.
Each attribute is shown with a filter control under the name of the attribute:
Sliders controls operate on numeric values
Filter buttons operate on date and string values
Manipulate these controls to filter the data types in the View.
When you click a Filter Attributes button, a list box opens that displays all available values for the selected attribute. In the image shown here, the DATE_DEATH attribute was selected and the discrete values for this attributes are displayed in the resulting list box.
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To hide or display values in a list box, click to uncheck or check each value in the list. If an item is checked, it will be displayed. If unchecked, it will be filtered. Once the values to be filtered are set, click the Filter button to apply the settings. To display all items that were previously filtered (unchecked), click Activate All and then click the Filter button. This will reset the filtering and re-display all of the objects or associations affected.
In this list, the  icon represents a value that is hidden. All objects with these values in this attribute are hidden from the View. A value with the  icon is a value that is visible. All object with these values in this attribute are displayed in the View.
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At the top of the Filter Panel is an Action drop-down and two buttons. The button on the left hides unconnected objects.
The button on the right beacons objects that were marked by the filter settings. You can mark items, rather than hide
them, by changing the action setting from Hide to Mark.
In the lower portion of the Filter Panel are three drop-down controls and two buttons, one marked Filter and
the other marked Reset. The three drop-downs, Links, Connections and Similar, are meta-data values used to filter data.
The Filter button applies an outstanding change to the View. The Reset button resets all changes made in the View Filter
Panel, essentially undoing any applied filter settings.
The image shown here is a portion of a link chart without filtering applied.
The image shown here displays changes to the range of the settings for the AGE object. It also shows that the DATE_DEATH attribute is filtered, as well.
This final image shows the result of applying these filter settings to the View. We are left with persons under the age of 31 (due to the change in the AGE attribute) and with dates of death that correspond to the values checked in the Filter Panel.
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Social Network Analysis (SNA)
An important form of link analysis that is growing in popularity is Social Network Analysis (SNA), sometimes referred to as Organizational Network Analysis. The purpose of SNA is to evaluate network nodes, and the relationships between them, from a "human" perspective. In this form of analysis, the significance of nodes is derived from each node's positional relationship to other nodes.
In SNA, nodes (objects in VisuaLinks) represent people, cities, computers, businesses or any other activity or process. The links between the nodes represent interactions of some form: phone calls, e-mail exchanges, conversations, chance meetings on the street, drug or weapon sales - the variety is limitless.
A key precept of SNA is that people tend to interact with people with whom they are already familiar and they tend not to step outside the confines of their known associates. Additionally, it is accepted that there is inherent value in the various interactions and relationships. This value is referred to as "Social Capital." In social networks, Social Capital influences interactions within a network.
SNA is the study and application of these, and other, concepts to determine nodes (usually people) in networks who are in some way "important" to that network.
With the release of VisuaLinks 3.0, we have implemented some SNA capabilities. Specifically, we have implemented a set of positioning algorithms, based on the concept of Centrality, that allow you to apply visual aspects of SNA to your own network analysis. This Link Chart of the Month looks at these positioning algorithms.
You may remember that we gave an overview of SNA in our May 2003 issue. As that issue provides information about Centrality and related concepts, we will leave further theoretical discussions as a reference exercise for the reader. In this article, we will look at the mechanics of using the SNA features of VisuaLinks 3.0.
We will begin our discussion with a look at telephone tolls. We begin our discussion with a simple query in a phone toll database to extract an item of interest. We then walk the query result a number of times to build a network of related phone calls. The result is shown here:
We drew a box (using VisuaLinks' new Presentation tools) around each successive data walk result. After five walks, we have a network of 17 phones.
There are four positioning algorithms in the Centrality placement: Degree, Relative Closeness, Absolute Closeness and Betweeness. Again, please refer to our May 2003 issue for detailed explanations. In that discussion, "Centrality" and "Degree" (in the VisuaLinks user interface) are synonymous.
Degree
"Degree" refers to an object's "connectedness." The more connections a node has, the more influence or control that node has the potential to wield within the network. When we apply this placement to the network we created above, we get the following:
The first image is displayed in a circular, or centric, pattern. The second is a hierarchical pattern which, like the Weighted placement, places the most-connected object in the upper left. In the circular display, the object closest to the center is the most-connected. The objects above and below that are the next-most connected and the outer ring are the least connected objects. Which layout you choose to use is entirely personal preference.
So what does this tell us? The object labeled "Buster Cardoza" is the most-connected object in this network. This indicates that this node could be highly influential in the network. Additionally, removing this node from the network would have a strong negative effect on the viability of the network.
Next we look at Closeness. Closeness is a measure of any given node's distance to all other nodes in the network. In VisuaLinks, Closeness is computed relative to a range between 0 and 100. This facilitates comparisons between networks of different sizes. The higher the number, the higher an object's Closeness.
When we apply the Relative Closeness, we see that Buster Cardosa and Whitney Shear share the same closeness value of 100.
Nodes with high Closeness are well connected within the network and can react swiftly to changes in the network because they are closest to all other nodes. Because of this, they can move information more quickly through a network as they will require few intermediaries to accomplish the task.
Our final Centrality Placement is Betweenness. Betweenness is a measure of which nodes are on the shortest paths between other nodes. Nodes with high betweenness control the information flow through a network. Such nodes are also sometimes referred to as "gatekeeper" nodes or liaisons. Nodes with high Betweenness can also act as single points of failure because the paths between so many other nodes pass through them.
Again, we see that Mr. Cardoso wins the Betweenness race.
In this small network, we can easily see that Buster Cardoso is likely a highly influential and crucial member of this network. By all measures of Centrality available in VisuaLinks, Mr. Cardoso is a key player in this network and would make a valuable target for an investigation. Also, removing Cardoso from this network would go far toward disabling it.
For more information on Social Network Analysis, please refer to the web references at the end of the May 2003 article.
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in the Query Results Panel's toolbar. Display the results tab you want to clear and then click the 
icon in the Details Panel's toolbar.
