Cambridge Intelligence

Major capabilities in KeyLines include support for a variety of layouts: standard, lens, hierarchical (sequential) and structural (where nodes are grouped into communities). For graph analytics it offers analysis by closeness, betweenness, page rank, Eigen centrality, degree and so on. Network filtering is available so that you can look at sub-networks. Social network analysis supports facilities to find both key players and major influencing paths as well as cluster detection algorithms. You can customise the appearance of the interface and any associated workflow. And there are geo-spatial capabilities that allow you to select any preferred map design, and to switch seamlessly between viewing the data in either network mode or map mode. In other words, all the sorts of capabilities you would expect.

However, KeyLines also has a couple of capabilities that you might not expect. The first of these is the time bar (the histogram at the bottom of Figure 1). This not only supports filtering by time but allows you to select specific periods to look at, to track changes over time and to watch nodes appearing and disappearing over time. You can combine time-based filtering with geo-spatial mapping, which Cambridge Intelligence reckons to be a significant differentiator for KeyLines.

Figure 2

The second unusual – as far as we know unique – feature of KeyLines, has just been introduced in the latest version. This is the ability to combine nodes by one or more pre-defined attributes, and open individual nodes for more detailed exploration. As an example of this, Figure 2 shows a telecommunications infrastructure organised by country and region. This is a drill-down from a network map, where the nodes had all been combined into London, Edinburgh, Paris and so on; but the user has opened the Edinburgh node to examine this in more detail. This approach makes it much easier to understand and investigate clusters of data, which can often become very complex because of large numbers of edges and nodes.

Graph visualisation is becoming more and more important, as organisations start to appreciate that relationships are all around us, and that they need to understand and leverage those relationships. Unfortunately, these relationships are both multiple and complex, and graph visualisation provides a way to explore these networks of things, people and events. But graphs can all too often look like spaghetti, so methods of simplifying the environment are important, as witness the pull-quote from Microsoft.

The Bottom Line

We like KeyLines. It does all the complicated stuff that you would expect, but it is its efforts to make graph analysis and visualisation simple that we particularly like.