This blog post takes a closer look at the organic layout, showing how it delivers performance and quality, giving the best of both worlds.
If you want graph visualizations that are insightful and beautifully presented, you have to rely on automated graph layouts. It doesn’t matter if your dataset is large or small, contains hierarchies or clusters. You’ll need a powerful layout that arranges items automatically, so you can spot patterns quickly.
When you’re applying layouts in other front-end visualization applications, there’s always a trade-off between speed and quality.
A force-directed layout may look great, but you have to wait for the algorithm to find the ‘ideal’ result and stop iterating. Apply the layout to significant data volumes and it’ll take even longer.
Alternatively, you could use a faster layout but just get ugly and unhelpful results. Bad layouts fail to untangle networks in a limited time, giving a confusing visualization that offers little insight.
Our graph visualization products have a range of powerful force-directed layouts to suit every scenario. The organic layout is our solution to the speed vs quality challenge. It’s great for any network type and is particularly good for larger datasets.
Identify underlying structures
When a user first opens a visualization, they often need a clear overview of the dataset. They want to see structures and patterns that guide them to the specific nodes and clusters they need to investigate further.
The organic layout makes these underlying structures easy to spot, by:
- reducing node and link overlap, so every element is visible
- minimizing white space in the chart, to give the closest possible view
- giving distance between different chart components, so it’s easy to spot clusters
As with any force-directed graph layout approach, the organic layout identifies connected subnetworks and uses three physical forces – repulsion, springs and network energy – to position them in the optimum location.
This approach is incredibly valuable when you’re working with larger, more complex networks. They make it easier to identify the underlying structure of each connected component, and compare large concentrations of items in different areas of the network.
There’s another clever algorithm going on under the hood: packing.
The organic layout packing algorithm
Packing handles networks containing groups of disconnected nodes. It arranges multiple components in a circular pattern with larger components in the center, giving a more natural, organic feel to your visualization.
There’s one final advantage to the organic layout that makes it especially useful for large datasets: its performance.
Faster graph visualization layout performance
We constantly optimize all aspects of our data visualization toolkits’ performance. The organic layout performs six to eight times faster than the standard layout, making it a particularly good choice for large networks.
You’d usually have to rely on your back-end integration to achieve performance enhancements like this. Delivering it as a front-end interaction is a quick win, and gives you far greater control and flexibility.
If you’re using our graph visualization SDKs, try one of our performance demos to see the organic layout’s impressive power for yourself.
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